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Record W3103240564 · doi:10.11141/ia.55.11

Reproducibility, Replicability, and Revisiting the Insta-Dead and the Human Remains Trade

2020· article· en· W3103240564 on OpenAlexafffund
Shawn Graham, Damien Huffer

Bibliographic record

VenueInternet Archaeology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsReplicateSocial mediaReproducibilityField (mathematics)Computer scienceStatisticsMathematicsWorld Wide Web

Abstract

fetched live from OpenAlex

The trade in human remains on social media happens in an ever-changing field of digital media technologies. We attempt to replicate our earlier study, exploring the differences in what we can observe now in the trade on Instagram versus our first foray in 2016 (published in Huffer and Graham 2017). While the previous study cannot be reproduced, it can be replicated, and we find that the trade is accelerating.

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)0.163
metaresearch head score (Gemma)0.491
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.837
Threshold uncertainty score0.864

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1630.491
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0070.007
Science and technology studies0.0050.037
Scholarly communication0.0130.015
Open science0.0040.010
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0090.001

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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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.

Study designQualitative
DomainReproducibility
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

Citations8
Published2020
Admission routes2
Has abstractyes

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