MétaCan
Menu
Back to cohort
Record W2124194484 · doi:10.12927/hcpol.2013.23399

How to Summarize a 6,000-Word Paper in a Six-Minute Video Clip

2013· article· fr· W2124194484 on OpenAlex
Pascale Lehoux, Patrick Vachon, Geneviève Daudelin, Myriam Hivon

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueHealthcare policy · 2013
Typearticle
Languagefr
FieldSocial Sciences
TopicParticipatory Visual Research Methods
Canadian institutionsMcGill University Health Centre
FundersCanadian Institutes of Health Research
KeywordsComputer scienceKey (lock)Process (computing)MultimediaVideo recordingWord (group theory)Production (economics)Linguistics

Abstract

fetched live from OpenAlex

As part of our research team's knowledge transfer and exchange (KTE) efforts, we created a six-minute video clip that summarizes, in plain language, a scientific paper that describes why and how three teams of academic entrepreneurs developed new health technologies. Recognizing that video-based KTE strategies can be a valuable tool for health services and policy researchers, this paper explains the constraints and sources of inspiration that shaped our video production process. Aiming to provide practical guidance, we describe the steps and tools that we used to identify, refine and package the key content of the scientific paper into an original video format.

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)0.005
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), 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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.509
GPT teacher head0.614
Teacher spread0.105 · 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