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Record W4398219317 · doi:10.24908/ohi.v2i1.17579

Lost Pets Sold to Research Laboratories: Addressing Pound Seizure Through an Educational Social Media Page ‘FetchFreedom’

2024· article· en· W4398219317 on OpenAlexaboutno aff
Kaitlin Mitchell, Isabel Farkouh, Daniel Keripe

Bibliographic record

VenueOne Health Innovation · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsnot available
Fundersnot available
KeywordsPound (networking)Social mediaPsychologyMedical educationAdvertisingMedicineBusinessComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Pound seizure is a wicked problem involving the sale of companion animals from shelters to educational or research facilities for experimentation. Because these companion animals are an inexpensive alternative to laboratory-bred animals, the issue remains widespread. Ontario is the only province in Canada where pound seizure is still mandated, and this is likely due to a lack of awareness among the general population. Pound seizure, however, is a wicked problem with significant impacts on humans, non-human animals, and the environment, including dangerous research outcomes, physiological and psychological pain and suffering, and environmental pollution. To address this issue, we have initiated an action involving the use of an educational Instagram page called FetchFreedom and a supporting social media campaign. Collectively, this action is intended to bring awareness to the issue and encourage others to take a stand against pound seizure.

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)0.002
metaresearch head score (Gemma)0.006
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: Other · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0050.002
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0210.003

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 head0.299
GPT teacher head0.499
Teacher spread0.200 · 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
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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