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#ItsNotInYourHead Campaign Materials - Provoked Vestibulodynia

2017· dataset· en· W6977143526 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2017
Typedataset
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsVulvodyniaPhoto elicitationMEDLINEPublic healthDistressing

Abstract

fetched live from OpenAlex

#ItsNotInYourHead is the hashtag associated with a campaign for a condition called Provoked Vestibulodynia (PVD), initiated by Dr. Lori Brotto and the Women's Health Research Institute in Vancouver, British Columbia. PVD is characterized by provoking pain with touch to the vulvar area, and affects up to 10% of women. The campaign aims to raise awareness about this condition and share evidenced-based research of psychological treatments that can help manage female genital pain.<br>These materials are meant to help promote the #ItsNotInYourHead campaign. They can be downloaded from this site - please be sure to use the hashtag and/or tag @NotInYourHead17 in all of your tweets!<br>

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.071
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0070.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1050.034

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 head0.050
GPT teacher head0.345
Teacher spread0.295 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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
Published2017
Admission routes1
Has abstractyes

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