1. Introduction

Supertyphoon Haiyan is one of the most damaging storms in modern times. When it made landfall on the eastern seaboard of the Philippines in late 2013, Haiyan packed winds of up to 195 mi h⁻¹ (87 m s⁻¹) and caused destructive storm surges in multiple provinces [National Disaster Risk Reduction and Management Council (NDRRMC) 2014]. Despite all of the warnings broadcast by the Philippine government and sent through media, Haiyan cost hundreds of millions of (U.S.) dollars in infrastructure damage and caused trauma for millions of Filipinos (NDRRMC 2014; See and Porio 2015).

When the damage was assessed, researchers and journalists asked, what went wrong? Some scholars blamed the media for making generic warnings that were not specific to the different locations in the path of the storm and for not highlighting the exact damage that a storm surge could do (Gomez and Cabilao-Valencia 2015; Jibiki et al. 2016; Leelawat et al. 2014; Lejano et al. 2016; Montemayor and Custodio 2014). Others blamed the government for using the term “storm surge” instead of words in a local language (Montemayor and Custodio 2014). To improve future disaster response, researchers and journalists called for more citizen drilling and training in disaster preparedness, better science journalism, and more science-based disaster response policies (Dator-Bercilla et al. 2017; Montemayor and Custodio 2014; World Bank 2016).

The work of rebuilding after Haiyan fell to the NDRRMC, the country’s national agency tasked with disaster risk management. The agency coordinates with the national weather bureau to pass severe weather warning information to NDRRMC’s offices at regional levels. These regional offices pass the information to provincial offices, which then pass the information to city or municipal offices. The mayor is the chair of the Disaster Risk Reduction (DRR) committee in their area of jurisdiction and is assisted by a DRR officer. The DRR committee passes information to the barangay (village) captains, who are elected officials, and who are required to pass information to their constituents. These captains work alongside barangay councilors, also elected officials, on a variety of barangay-based affairs, including disaster response. In some cases, barangay captains are assisted by a network of barangay health care workers (BHW). The BHWs are citizen volunteers who are assigned to even smaller community units and work closely with both citizens and barangay officials. After Haiyan, the NDRRMC (2014) blamed local governments for not passing on scientific information to the barangays, since, the agency assumed, knowing more about weather science would have prepared people for Haiyan. One of the NDRRMC’s solutions to the issue of passing understandable scientific information was translation of the term “storm surge” into the Filipino daluyong in later warning messages.

The long chain of risk communication outlined here is similar to chains of information documented in different parts of the world (Das 2019; Lindell et al. 2007; Paul 2012). Such a chain is necessary when governments need to spread information urgently through a broad network, from a weather service to those who live in at-risk areas. This model can also be used to evaluate the efficiency of message dissemination later, assuming that there is some degree of mathematical predictability of how fast and far information can travel. This model assumes that scientific information need only be disseminated to an audience; the model also assumes that the audience requires only scientific information to make a risk-related decision (Lindell and Perry 2004; Trench 2008) and that all vulnerable groups will act the same way when disaster strikes (Das 2019).

Researchers acknowledge the shortcomings of the model: reliance on dissemination tends to prioritize pushing information over studying how the audience understands information and why the audience might act on the same information in different ways (Lindell and Perry 2004). Different factors such as source, channel, and audience all play a part in the decision-making process (Lindell and Perry 2004; Trench 2008). Risk communication researchers also acknowledge that the outright assumption of an ignorant audience can keep researchers from uncovering aspects of risk communication that go beyond an audience’s need for information; newer models such as the Protective Action Decision Model (Lindell and Perry 2004) or even constructivist and critical approaches (Trench 2008) take into account the impacts of peer networks, meaning making, the message sender’s social milieu, and culture.

The stress on information nevertheless remains at the center of disaster risk reduction practice. To DRR professionals, extensive drilling and training, as well as disaster education, should be enough to make people obey authority and develop a risk imagination (Cutter et al. 2015; Gomez and Cabilao-Valencia 2015; Montemayor and Custodio 2014; Norton et al. 2011; World Bank 2016). To researchers in the risk communication literature, warnings should explicitly show and explain the impact of a storm on a specific location, why it is a threat, and what should be done (Baker 1991; Bean et al. 2015; Huang et al. 2016; Lindell 2018).

These recommendations arise from years of studying people who live in hazard-prone areas. For instance, most residents of New Zealand and the United States will evacuate when they receive warnings about the specific damage that a storm can cause in specific places, and especially if such warnings come from authorities and public officials (Baker 1991; Dow and Cutter 1998; Lindell 2018; Potter et al. 2018). Audiences tend to look at the credibility of their officials (Lindell 2018) and if the messages explain what people will experience and what they should do (Baker 1991; Huang et al. 2016; Lindell 2018). Such impact-based warnings can increase concern about a threat regardless of the language used (Potter et al. 2018) and even spur evacuation, in the case of tornadoes (Casteel 2018). If a warning lacks specificity about the threat or the affected area, then its audience will passively monitor the situation without actively seeking more information on what to do (Lindell and Perry 2012).

Television and radio are the most used media channels for hurricane evacuation in the United States, and access to these media channels is fairly high in hurricane hazard-prone communities (Baker 1991; Lindell 2018). When there is little to no access to media, as in the case of the 2013 floods in India, people will prioritize environmental cues and peer warnings over official warnings when making evacuation decisions (Lindell et al. 2019); in some cases, peer warnings are a primary source of information (Baker 1991; Lindell 2018; Wood et al. 2018). Both environmental and social cues can lead to an expectation of severe personal impact, which might then lead to evacuation (Huang et al. 2016).

When they have no previous experience with a violent storm, people tend to mill. They might look at how their peers behave (Wood et al. 2018) or wait for more information to confirm warnings (Lindell 2018; Lindell et al. 2019). People might evacuate if they see their peers preparing to leave (Baker 1991; Lindell 2018) or if they see businesses closing (Lindell 2018). The act of milling promotes emergent norms, which, in turn, become part of the community’s disaster subculture (Lindell 2018; Wood et al. 2018). The disaster subculture comprises patterns in community behavior in a specific area, which arise due to a common, periodic hazard; these might include norms, beliefs, habits, or even stories passed from one generation to the next, which later generations or community newcomers can adopt as a blueprint for behavior when the hazard recurs (as seen in Gaillard et al. 2008a).

That is not to say that previous experience is always a guarantee of evacuation. While prior experience can make people more aware of possible hazard damage (Baker 1991), this experience may need to be direct, rather than vicarious, as in the case of the Chilean tsunami (Bronfman et al. 2020); even then, the connection between experience and action later is mediated by different variables that determine the conclusions that are drawn from that experience (Demuth et al. 2016). Moreover, some people can fall victim to false experience: they think they have already felt the strongest storm possible and cannot imagine the impact of a more violent, destructive storm (Baker 1991; Lindell 2018). A similar phenomenon of normalcy bias has also been observed: people who might have been on the other edges of a strong storm in the past might not take future warnings about strong storms later on, because they believe that what they experienced already exemplifies the storm’s worst conditions (Drabek 1986; cited in Mayhorn and McLaughlin 2014).

Research has also shown that personal experience of a storm alone does not always motivate people to reduce risks; risk reduction and coping can be a social endeavor. In flood-prone Alpine regions, knowledge and experience of flooding made residents more aware of flood risk, but strong community ties encouraged them to actually prepare for floods (Scolobig et al. 2012). In the Philippines, flood-prone rural communities recovered faster if there were stronger community ties to provide emotional and social support (Gaillard et al. 2008b).

The diversity of experiences with warning messages across national and cultural borders has prompted researchers to call for more studies into the sociocultural factors that drive how different communities understand information, process warnings, and construct risk (Lejano et al. 2016; Priest 1995; Wibeck 2014), as well as how warnings pass from government to the people and how the messages are received (Lindell et al. 2007; Wei et al. 2014).

Such research has already been done in Bangladesh and India: both countries are periodically hit by damaging cyclones and are home to a variety of both urbanized and coastal communities comparable to those in the Philippines. In Bangladesh, those who did not evacuate had a false sense of security: they had not attended disaster preparation training, and perhaps did not know what to expect from a storm (Ahsan et al. 2016; Paul 2012). Some communities also found the warnings complicated or fast changing and were more concerned about shelter cleanliness and availability (Paul et al. 2010). These communities did have some local-level warning dissemination; trust in a warning was an important factor that determined evacuation (Paul 2012).

In India, evacuation was associated with previous traumatic experience with storms, local knowledge, the orders of village committees, and trust in one’s community (Das 2019; Sharma et al. 2009; Walch 2018). People tended to mill as a result of false experience: they did not evacuate because they did not think a storm would be so severe (Walch 2018). They would then wait for more information or depend on environmental cues for evacuation if the warnings did not match their current surroundings (Sharma et al. 2009). Evacuation was a community decision, with some communities waiting for elders’ advice; conversely, communities with lower levels of social cohesion tended to have lower levels of evacuation (Das 2019; Sharma et al. 2009).

In the Philippines, prior research into experiences with Haiyan has been largely concerned with how warnings were understood (Gomez and Cabilao-Valencia 2015; Jibiki et al. 2016; Leelawat et al. 2014; Lejano et al. 2016; Montemayor and Custodio 2014). An exception is Robles and Ichinose (2016), who conducted surveys of Leyte and found that strong social networks allowed Haiyan victims to recover faster from the disaster because they saw the storm as a shared experience that strengthened community bonds. Walch (2018) also documented the experience of Tacloban: people underestimated Haiyan because of its past experience with weaker storms, low trust in local officials, and uneven local governance; they turned to broadcast and social media for information.

While this research can be useful in crafting future warnings, it tells us little about the diversity of understandings of storms for other communities across the Philippines. Studying how other communities understand and process warnings is significant in the Philippine hazard response context: the country has hundreds of languages and cultures in over seven thousand islands, all of which are vulnerable to storms, and all of which might have diverse ways of consuming warning messages. This researcher has already carried out work in two different barangays (villages) on the eastern seaboard of the Philippines. “Coastal,” the pseudonym for a seaside barangay, and “Poblacion,” the pseudonym for a city barangay vulnerable to storm surges, are located in Guiuan, Eastern Samar (Ponce de Leon 2020a), the site of Haiyan’s first landfall. A similar study was conducted in two barangays in Palo, Leyte (Ponce de Leon 2020b), a municipality heavily damaged by Haiyan’s storm surges. Both Guiuan and Palo are hit yearly by strong typhoons, so studying their response to Haiyan provided a perspective of how citizens responded to extreme weather warnings. These studies tracked information from the mayor and his DRR officer to the two barangays at each location.

In Guiuan, the researcher found that the local government assumed that giving orders and information were enough for people to take action. The Coastal community remembered these warnings but assumed that they would be protected by the Virgin Mary and would therefore not need to evacuate. They could not imagine a storm based on forecast figures and waited for their environment to match warnings before they chose to evacuate. The Poblacion community, while information savvy, simply waited for evacuation orders and relied on both actual and vicarious experience to make decisions. For example, the citizens wished the storm surge had been called a tsunami in order to match the damage they had seen tsunamis do on the news. Both communities trusted the local weather bureau and their barangay captains for information on what to do.

In Palo, Leyte, there was no communication framework or system for reaching out to communities: the local government ordered people to evacuate and called them “hard headed” if they refused. The Coastal community did not evacuate because they believed they would die if it was God’s will. They also believed that the dry weather, which preceded Haiyan, had also been a sign that the storm would not come. They simply wanted evacuation orders for later storms rather than information about the storm’s magnitude. The Poblacion community, on the other hand, focused on relief rather than preparation and resented their barangay officials for not giving them food during Haiyan. Like the Coastal community, they wanted to be told to evacuate; however, they did not trust their government.

This previous research covers communities that are often hit by violent storms: these communities might be expected to already have systems in place to deal with storms, or habits and attitudes that have been ingrained through years of exposure to extreme weather events. There are communities, however, that are not accustomed to such hazards. Their experience with a violent storm can reveal new routines and emerging habits the community can later use to deal with the hazard, if and when it comes again.

In this paper, the author documents the results of the same data gathering and analysis strategy used in Guiuan and Palo. This time, the researcher examines Coron, Palawan: the location of Haiyan’s last landfall, and a municipality that was not often hit by violent storms prior to Haiyan. Coron represents communities that are new to extreme storms and that might have emerging habits of dealing with hazards. Coron’s case prompts the question: how do communities that have little experience with violent storms understand storm-related warnings?

2. Theoretical framework

This research is guided by S. Hall’s encoding–decoding theory from 1977 (as cited in During 1993). Encoding–decoding theory is a cultural studies framework that describes the phenomenon of communication, but neither prescribes nor predicts an outcome. Rather, the theory focuses on the cultures that surround both those that create messages and those that receive them. The model suggests that an institution (the encoder) crafts and sends a message based on institutional structures, frames of knowledge, and assumptions about its audience. The audience (the decoder) reads the message, understands it, and acts on it through the filters of culture, frames of knowledge, and social forces. The social forces and frameworks of knowledge can differ between the encoder and decoder: the decoder can draw from its own experience and social context, or even change the message itself through collective action (Aligwe et al. 2018). What is in focus, therefore, is not whether a message survives intact, but the social worlds surrounding both the encoder and decoder.

Encoding–decoding theory does not assume that a message will have a direct, predictable behavioral effect on the audience: it allows the audience to act on a message even in ways opposite to that of the encoder (Demeritt and Nobert 2014). The theory allows for dissemination to occur, but it does not assume that all people will have the same understanding of the message. Hall himself criticized the assumptions of prescriptive information dissemination models on these grounds, hence the theory, which forwards the need to first study an audience before designing any message for them (Aligwe et al. 2018).

Encoding–decoding theory, as a conceptual framework, fits in this study because it works within the infrastructure of information dissemination being used in the Philippines, but allows that same model to be critiqued and nuanced. Using this theory, moreover, addresses Lindell et al.’s (2007) call for research on how people understand the warnings disseminated to them, and adds detail to Lejano et al.’s (2016) research on risk communication chains during Typhoon Haiyan.

In this research, the theory is used to investigate how storm warnings were disseminated and understood in Coron during Typhoon Haiyan. In examining how the warnings were understood, the researcher also hopes to shed light on the social worlds of the local government and its constituents in two locations, whose names have been hidden under pseudonyms for this study: Island, a coastal barangay; and Central, a barangay close to the municipal center. It is assumed that these two barangays can represent varying locations with distinct characteristics because of differing experiences with natural hazards. These differing experiences might also translate into differing paths of warning information, and therefore different cultures that have different decisions on the same warning message.

3. Methods

In terms of encoding–decoding theory, the local government of Coron, including its mayor and DRR officer, represented the encoders. There are two groups of decoders for each barangay: the elected officials and the BHW, who are citizen volunteers and can represent the general population. In this study, focus group discussions (FGDs) were conducted with a mixture of both BHWs and citizens. The researcher interviewed the encoders, and then carried out FGDs with each group of decoders at each location. This process translated to two separate interviews with the Coron local government and two FGDs each for Central and Island.

Local contacts were asked to recruit volunteer discussants so that the FGDs could occur at a time and place most convenient to the participants. Not all the FGDs, therefore, were filled with the ideal 5–7 people for a communication research FGD. The researcher did not demand that the contacts add more FGD participants. The researcher allowed the contacts to work within the limits of their schedules and duties so that the researcher could establish and maintain rapport with the community.

Both the interview (Table 1) and FGD (Table 2) questions were guided by encoding–decoding theory. The questions were not asked in strict order; instead, the interviews and FGDs flowed through the concepts covered by the questions depending on how participants responded, with no interruption or ideas from the facilitator. The participants were encouraged to elaborate on their answers and to be as candid and truthful as possible.

Table 1.

Interview questions for mayor and DRR officer, with corresponding concepts being addressed.

View Table

Table 2.

Focus group discussion questions for both captains/officials and BHW/citizens, with corresponding concepts being addressed.

View Table

The researcher transcribed and then analyzed all interviews and FGDs using modified typological analysis protocols (Hatch 2002). The researcher coded the data using 10 major codes matching previous research on disaster response (Lindell and Perry 2012; Wei et al. 2014) and covering Hall’s concepts of social forces and frames of knowledge (Table 3). The researcher coded the interview transcripts with an additional code of institutional cultures to comprise the local governments’ assumptions of their constituents.

Table 3.

Codes for analyzing interviews and transcripts.

View Table

The researcher read the coded data under each code and wrote the observed patterns (repeated words/phrases), relationships (connections), and themes (abstractions), first within each code and then across all codes. These patterns, relationships, and themes within and across codes were assembled into a master outline to represent the flow of information from the local government to constituents, and for two separate locations in Coron. This systematic, qualitative coding of the data lends depth to previous survey-driven research and allowed the researcher to examine how people in specific contexts understood warning information based on their social worlds.

The master outline guided the flow of the results and discussion section of this paper. The major themes of the outline are supported by direct quotations from the data, which the researcher translated from the native Filipino. Some quotations have been edited for brevity, such as by removing nonessential words or phrases (denoted by three-point ellipses) or whole sentences (denoted by four-point ellipses). The quotes appear in indented, single-spaced text, to set them apart from the main discussion. Because of the nature of the findings, no actual names are used; all participants are given pseudonyms based on their position (Mayor, DRR, Captain, Councilor, Worker) and then serial numbers based on who spoke first during the FGD (Councilor1, Councilor2, BHWC1, BHWC2, etc., where the BHWC is the acronym that unites BHWs and Citizens).

4. Results and discussion

Coron is a major tourist destination on the eastern seaboard of the Philippines and is bounded to the west by the South China Sea. It comprises 23 barangays scattered across two large islands and 50 islets (Department of Trade and Industry 2020). Typhoon Haiyan damaged all hotels, cut off power to nearly the entire municipality, and stranded hundreds of tourists. While the storm did not cause surges, residents said that the sea swelled, and some areas of the municipality were flooded. The mayor at that time (who could no longer be contacted for an interview) claimed that citizens did not evacuate because typhoons hardly came to Coron (Legaspi 2013).

When asked if he had suggested such communitywide drills to the mayor, he said that the mayor told him to simply “tell people what to do” and “stop with all the training because it is a waste of time.” When the researcher suggested that the DRR officer continue his training, and then train his own constituents in a Coron-specific disaster response program, he said he was delighted by how the plan could “empower people on the ground.” These statements indicate that the mayor perceives communication as a way for an obedient population to follow orders. In opposing this view, the DRR officer perceives communication as a way to capacitate communities so that they can be called on to help themselves.

This clash at the level of encoding shows two opposing government cultures: one that believes in long-term, proactive work to encourage capacity, and hence the need for communitywide drills and training, versus one that believes in short-term, reactive tasks to keep people in line, and hence the simple “tell them what to do.” In 2019, the Coron mayor was voted out of office, but the DRR officer remained and continued training. The latter was able to secure early flood detection systems for the municipality through years of work with the private sector (Magdayao 2019).

5. Conclusions

Supertyphoon Haiyan caused massive damage to the Philippines in 2013 and forced the Philippines to examine the country’s hazard response systems. Disaster risk reduction professionals recommended more drills and disaster education (Cutter et al. 2015; Gomez and Cabilao-Valencia 2015; Montemayor and Custodio 2014). Researchers claimed that the warnings should have been more specific and descriptive for each province in Haiyan’s path (Gomez and Cabilao-Valencia 2015; Jibiki et al. 2016; Leelawat et al. 2014; Lejano et al. 2016; Montemayor and Custodio 2014). But can simply adding more drills and changing the content of warnings be enough to spur behavior, especially for locations that have little experience with supertyphoons? To address this, the researcher used S. Hall’s encoding–decoding theory and studied two locations in Coron, Palawan, a major tourist destination in the Philippines.

Interviews with local government officials, and discussions with village officials and citizen volunteer groups, showed that each stakeholder group responded differently to Haiyan warnings. At the municipal level, the mayor wanted people to simply obey their government, while his DRR officer wanted to empower people to think and do for themselves. This contrast also appeared at the community level: Island had indigenous knowledge of hazards but chose to wait for orders and aid from the mayor; Central worked proactively as a community to watch out for and respond to storms.

This researcher’s previous findings on two municipalities on the Philippines’ eastern seaboard (Ponce de Leon 2020a,b) contrasts with findings from the western seaboard municipality of Coron. In general, local governments assumed that people were ignorant or hardheaded if they did not heed warnings. At the community level, however, there were some distinct differences in how citizens responded to storm warnings. On the eastern seaboard, towns fell victim to false experience, but with a strong religious component; post-Haiyan, they wanted explicit evacuation orders and more descriptive warnings. On the western seaboard, Island wanted explicit evacuation orders, while Central wanted to document storm impacts and work proactively as a community. Translation of the term “storm surge” also differed among the three communities: the eastern seaboard municipalities wanted the storm surge to be called a tsunami to match what people saw on the media; Coron did not want the term to be translated at all.

These findings indicate that across the Philippines, different localities might have varying understandings of the same risk: localities that are used to storms might treat them as a usual occurrence, hence the need to simply be told to evacuate; localities that are slowly getting to know violent storms are more open to information and learning as they mold their systems to meet their community capacity for hazard response. Nevertheless, care must be exercised in interpreting these findings: the focus groups were small and might not necessarily have represented all the views in the community.

Previous work on Haiyan-hit communities has focused mainly on municipalities that experienced traumatic storms (Gomez and Cabilao-Valencia 2015; Jibiki et al. 2016; Leelawat et al. 2014; Lejano et al. 2016; Walch 2018). This paper provides a perspective of a municipality that does not have such experience, which can therefore give researchers a glimpse into emerging disaster subcultures. The Coron communities echoed previous research that claimed that warnings did not highlight storm impacts for their specific locations (Gomez and Cabilao-Valencia 2015; Jibiki et al. 2016; Leelawat et al. 2014; Lejano et al. 2016; Montemayor and Custodio 2014), but with some nuance: Island wanted photographs to show personal impact, while Central wanted visual language to show what would happen in their immediate environment.

These findings on Coron add new layers of meaning to previous research on storm warnings. Like communities from New Zealand and the United States (Baker 1991; Dow and Cutter 1998; Huang et al. 2016; Lindell 2018; Lindell and Perry 2012; Potter et al. 2018), Coron trusts its leaders and acts on impact-based warnings that tell people what they should do. These impacts, however, can still be parsed into personal experience impacts and immediate environment impacts; the warnings can be in the form of photographs or descriptive language, depending on the community. These details should encourage further research into how different levels of storm impact can change how a warning message is consumed. These details might also be connected to variables that mediate the role of previous experience, as elucidated by Demuth et al. (2016).

As with U.S. communities, Coron trusts TV and radio as media channels (Baker 1991; Lindell 2018), but with new aspects that future research can examine in other communities. Island, despite access to broadcast media, still waits for local officials to order evacuation even when it knows how to read environmental cues. Central has a peer warning system complementing media consumption: people talk about what they watch and then act on it, all while trusting their leaders to organize them.

Coron, like other communities that had never experienced traumatic storms, fell victim to false experience and normalcy bias, and milled before Haiyan struck. Like communities in Bangladesh and India (Ahsan et al. 2016; Das 2019; Paul et al. 2010; Paul 2012; Sharma et al. 2009; Walch 2018), Coron did not know what to expect; the communities waited for environmental cues to match their mayor’s warnings. But while Haiyan created new norms in Central, Island chose to simply keep on waiting for aid in succeeding storms. Future research should examine why some communities continue to mill and investigate connections between aid dependence and warning message consumption.

In terms of character, Coron communities seem to have high levels of social cohesion, a factor previously identified as an important determinant of evacuation (Das 2019; Sharma et al. 2009; Walch 2018). This social cohesion, however, seemed to unite people in milling and waiting for aid, in the case of Island. Central, on the other hand, is more united as a community and proactive in its approach to hazard response.

This current research responds to calls for more investigation on how people receive, understand, and process warnings, information, and risk (Lejano et al. 2016; Priest 1995; Wei et al. 2014; Wibeck 2014). While previous research advocates for better information dissemination (Dator-Bercilla et al. 2017; Montemayor and Custodio 2014; World Bank 2016), Hall’s encoding–decoding theory can provide new perspectives into how different social forces can drive how a warning message is understood, above and beyond the information it contains. Future research should consider using encoding–decoding theory to critically examine the social worlds of different stakeholders in risk communication, and for different communities across a larger range of natural hazards.

While researchers and professionals recommend translating scientific terms, disaster education, and location-specific impact-based warnings (Baker 1991; Bean et al. 2015; Cutter et al. 2015; Gomez and Cabilao-Valencia 2015; Huang et al. 2016; Lindell 2018; Montemayor and Custodio 2014; Norton et al. 2011; World Bank 2016), these recommendations should be set against the context of specific communities. In the case of Coron, these message-level recommendations can be complemented by training programs to improve community capacity. Central can train Island in establishing a system of relief and communication that works even before a hazard arrives; Island can train Central in reading the environment and understanding hazards in their unique location. In so doing, both barangays can benefit as they exercise long-term, systematic planning while recognizing science in their lived environment, just as their DRR officer envisions.

This research has implications as well for science journalism and media coverage. The communities in this study relied on national TV news; but the Philippines comprises over 7000 islands with varying environments, and it cannot fall to national news to contextualize warnings for each location. Local news networks can set up community-based information systems to create context-specific broadcasts. Media coverage should consider including personal experience and environmental impacts as part of their warnings and not simply rely on disseminating storm-track information from the weather bureau.

This research, moreover, shows that constructions of risk and understanding of warnings can differ even between communities in the same locality. These findings should spur further research into the role of local knowledge and culture in consuming warning information, above and beyond merely translating scientific terms into local languages.