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Record W4414097322 · doi:10.1002/pan3.70140

Reimagining habituation: The case for a reciprocal and contextual understanding

2025· article· en· W4414097322 on OpenAlexafffund
Ethan D. Doney, Tom Fry, Valerio Donfrancesco, Hanna Pettersson, Sahil Nijhawan, Douglas A. Clark, Clemens Driessen, Christine Ampumuza, Chris Sandbrook

Bibliographic record

VenuePeople and Nature · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Ecology and Conservation
Canadian institutionsUniversity of Saskatchewan
FundersMitacs
KeywordsHabituationCognitive reframingContext (archaeology)ReciprocalProcess (computing)MainstreamPerspective (graphical)

Abstract

fetched live from OpenAlex

Abstract As the frequency and intensity of human–wildlife interactions continue to rise, the process and outcomes of habituation are becoming more important. Commonly defined as ‘…a waning of response to a repeated, neutral stimuli’ or of similar wording, we argue that these conceptualisations of habituation are too simplistic in the context of direct human–wildlife interactions. We argue that much of the habituation literature has been one‐sided (i.e. focused only on the nonhuman) and detriment‐focused, failing to grasp the deep complexities of the process and its implications. We conducted a brief scoping review of the habituation literature to show how the term is being used by whom, and in what context. We sought to explore habituation from a broad disciplinary range and therefore included literature from ethology, behavioural ecology and conservation biology as well as disciplines less represented in mainstream conservation such as multispecies anthropology, political ecology and more‐than‐human geography. Supported by the scoping review, we illustrate that habituation as an outcome of human–wildlife interactions is (1) a nuanced, reciprocal process that is both understood and practised in diverse ways, with potentially negative and positive impacts for both people and wildlife and (2) is shaped by cultural, historical and political–economic contexts. We share four case examples based on our own research that justify and reinforce our arguments for reframing our understanding of habituation. Adopting more reciprocal and contextual conceptualisations of habituation will improve our collective understanding of how it occurs and how to find ways to adapt and coexist. We urge future research to explore these ideas and understandings through different geographical and species contexts and apply additional disciplinary approaches to understanding and managing human–wildlife interactions. Read the free Plain Language Summary for this article on the Journal blog.

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 score0.037
Threshold uncertainty score0.267

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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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