MétaCan
Menu
Back to cohort
Record W4392609570 · doi:10.33137/ijournal.v8i2.41038

“I saw a cool bird today”

2023· article· en· W4392609570 on OpenAlexvenueno aff
Chloe Chaitov

Bibliographic record

VenueThe iJournal Student Journal of the Faculty of Information · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicGeographies of human-animal interactions
Canadian institutionsnot available
Fundersnot available
KeywordsAeronauticsEngineering

Abstract

fetched live from OpenAlex

This exploratory study examines the ways in which birders “read” nature while birdwatching. Looking (figuratively) through the binoculars lens of embodiment, it explores questions about how birders interact with nature as an information source and what role the senses play in their information experiences. Three non-professional female birders were interviewed using the information horizon (IH) technique, a method that elicits both visual and verbal data. Using an inductive thematic approach, the study finds that the practice of birding relies first and foremost on purposeful sensory attention and that established tools such as field guides and lists can become part of the corporeal experience. Further, it suggests that bodily experience—not definitive species identification—represents the key object of the overall activity. Utilizing insights from library and information science (LIS) and sociological theories, the study presents a new illustrative model of the “birding cycle of knowledge” in which embodiment acts as the anchor and gateway to navigating an informationally-rich world. Finally, a larger, in situ investigation of the social aspects of embodiment in birding is identified as a future research opportunity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.003
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.034
GPT teacher head0.362
Teacher spread0.328 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueThe iJournal Student Journal of the Faculty of InformationSame topicGeographies of human-animal interactionsFrench-language works237,207