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Record W4386056474 · doi:10.1111/acv.12900

Towards ecotourism that nurtures local visions for landscapes and wildlife

2023· article· en· W4386056474 on OpenAlexaff
Harold N. Eyster, Robin Naidoo, Kai M. A. Chan

Bibliographic record

VenueAnimal Conservation · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsVisionWildlifeSustainabilityLibrary scienceColumbia universityGeographyEcologySociologyMedia studiesBiologyComputer scienceAnthropology

Abstract

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We thank Scholte, Kamgang, & Sabuhoro (2023) and Hausmann (2023) for their perceptive commentaries on our study of what attracts ecotourists to Sub-Saharan African protected areas (Eyster, Naidoo, & Chan, 2022). Because our study relied on tourist visit data that lacked any demographic information, we were unable to differentiate between the preferences of different tourist segments, but we agree with Scholte, Kamgang, & Sabuhoro (2023) that this is a critical next step in understanding how ecotourism can aid conservation. Specifically, domestic, intra-African, and diasporic ecotourists likely have different “ecotourist gazes”—i.e., “way[s] in which tourists view the people and places they visit”—that our paper was unable to disentangle (Stone & Nyaupane, 2019, p. 2; Lindsey et al., 2007; Urry, 1992). Indeed, our results that bird diversity appears to matter to tourists may be particularly representative of intra-African ecotourists: Lindsey et al. (2007) showed that bird diversity was much more important to African than non-African visitors to South African protected areas. Disentangling the varied preferences of different types of tourists will be essential to help protected areas adapt to the possibility of future pandemics and growing African populations (Scholte, Kamgang, & Sabuhoro, 2023). Our study sought to examine the importance of the Big Five across Sub-Saharan Africa (specifically: elephants, rhinos, lions, buffalo, and leopards), but we agree with Scholte, Kamgang, & Sabuhoro (2023) that regional analyses are key to supporting the full diversity of African protected areas and wildlife. In particular, Scholte, Kamgang, & Sabuhoro (2023) note the longstanding underrepresentation of Central African wildlife and ecotourism in the published literature. Our study did not consider range-restricted species like mountain gorilla (Gorilla beringei), which is found only in the forests of the Congo basin. While we believe that general, large-scale analyses examining continent-wide patterns are useful, complementing this level of insight with analyses of the relationships between regional species and ecotourists will be key for sustaining protected areas across the continent. Hausmann (2023) suggests that social media data might help fill these research gaps. Images, text, and videos shared on social media platforms could indeed help reveal “tourist gazes” (Stone & Nyaupane, 2019). Moreover, these data could better show the variation in tourist gazes across locations and ecotourist origins. Yet Hausmann also cautions that these data must be critically considered, since they may exaggerate certain shareable ‘tourist imaginaries’ and underrepresent less viral but still important elements of the relationships ecotourists have with protected areas. Qualitative data may help to most productively interpret social media data (Snelson, 2016). Beyond describing existing meanings, researchers can investigate how the relationships that ecotourists forge with their destinations might be leveraged to promote locally appropriate conservation and restoration in those regions (Olmsted et al., 2020). Hausmann (2023) also reiterates our worry that ecotourist priorities and preferences may supersede those of local residents. Thus, in addition to clarifying the various preferences of different types of ecotourists, we must also seek to integrate these with the values, priorities, and preferences of the people who live amidst protected areas, including especially the longstanding residents of these places (e.g., Kihima & Kimura, 2013). Doing so requires a deep commitment to understanding these local relational values, including representing them on their own terms, rather than forcing them into the ‘rational’ logic of economic or Western values (Satterfield et al., 2013; Chan et al., 2016; Whyte, 2018; Gould et al., 2019; IPBES, 2022). We echo many African scholars in suggesting that this research will require “global science” that pluralizes understandings of African ecotourism, including through recognizing and supporting the contributions of African scholars (Asase et al., 2022). For example, a recent analysis of Neotropical ornithology found that much natural history data on neotropical birds were listed as nonexistent by the English-language Birds of the World platform, despite the existence of published descriptions in other languages, often in regional journals (Soares et al., 2023). This lack of recognition of Global South scholarship holds true for other regions beset by colonization, including Africa (Asase et al., 2022; Odeny & Bosurgi, 2022). Scholars who are not native English speakers face particular challenges (Amano et al., 2023). We wrestled with how best to involve African scientists in our own work (those residing in Africa, in addition to our foreign expertise). With the benefit of hindsight, we might have done more to ensure that our own large-scale analysis was also informed by an African scholarly perspective, or three. Positionality statements, such as the one that we included in our original paper, resulting from reflexive practices (Boyce, Bhattacharyya, & Linklater, 2022) can help to make clearer the perspectives and insights that different studies provide, the lenses they use, and the parts of the puzzle they interrogate (Eyster, Satterfield, & Chan, 2023), and thus help enable more pluralistic discourse around African ecotourism (Krenčeyová, 2014; Mwangi, 2019). Now, more than ever, understanding ecotourism is critically important for determining how to support conservation and local communities. We hope that researchers and practitioners can seize on the recommendations for future work that Scholte, Kamgang, & Sabuhoro (2023) and Hausmann (2023) outline, with particular attention to pluralistic understandings of both ecotourists and local residents.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.255
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2023
Admission routes1
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