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Record W2487827654 · doi:10.13034/jsst.v9i1.148

The Science of Tears

2016· article· fr· W2487827654 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Student Science and Technology · 2016
Typearticle
Languagefr
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtArt history

Abstract

fetched live from OpenAlex

When was the last you cried? Maybe it was while you were watching a sad movie or when a loved one was leaving you or because you just felt lonely. The next thing you know, you have a lump in your throat, your eyes start to water and tears are running down your cheeks. Considering that crying is an important and common part of everyone’s lives, many of us know surprisingly little about it.À quand remonte la dernière fois que vous avez pleuré? Peut-être que c’était lorsque vous étiez en train de regarder un film triste ou quand un proche vous a quitté ou parce que vous vous sentiez seul. Tout d’un coup, vous avez la gorge serrée, vos yeux deviennent humides et les larmes commencent à couler sur vos joues. Comme pleurer joue un rôle important de la vie de tous, beaucoup d’entre nous savent étonnamment peu à ce sujet.

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.

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)0.013
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.554
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.013
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.030
GPT teacher head0.427
Teacher spread0.396 · 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