MétaCan
Menu
Back to cohort
Record W3117217961 · doi:10.5703/1288284317182

Should You Pay for the Chicken When You Can Get It for Free? No Longer Life on the Farm as We Know It

2020· article· en· W3117217961 on OpenAlexaff
Sharon M. Mattern Büttiker, James King, Susie Winter, Crane Hassold

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicResearch Data Management Practices
Canadian institutionsPurdue Pharma (Canada)
Fundersnot available
KeywordsAttendancePanel discussionViewpointsPublic relationsPublishingScholarly communicationLibrary scienceSociologyPoint (geometry)ManagementPolitical scienceBusinessComputer scienceAdvertising

Abstract

fetched live from OpenAlex

The scholarly publishing ecosystem is being forced to adapt following changes in funding, scholarly review, and distribution. Taken alone, each changemaker could markedly influence the entire chain of research consumption. Combining these change forces together has the potential for a complete upheaval in the biome. During the 2019 Charleston Library conference, a panel of stakeholders representing researchers, funders, librarians, publishers, digital security experts, and content aggregators addressed such questions as what essential components constitute scholarly literature and who should shepherd them. The 70-minute open dialogue with audience participation invited a range of opinions and viewpoints on the care, feeding, and safekeeping of peer-reviewed scholarly research. The panelists were: James King, Branch Chief & Information Architect at the NIH; Sharon Mattern Büttiker, Director of Content Management at Reprints Desk; Crane Hassold, Senior Director of Threat Research at Agari; and Susie Winter, Director of Communications and Engagement, Springer Nature. The panel was moderated by Beth Bernhardt, Consortia Account Manager at Oxford University Press. Beth posed questions to the panel and each panelist replied from their vantage point. The lively discussion touched on ideas and solutions not yet discussed in an open forum. Such collaborative approaches are now more essential than ever for shaping the progress of the scientific research community. In attendance were librarians, editorial staff, business development managers, data handlers, library collection managers, content aggregators, security experts and CEOs.

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)0.005
metaresearch head score (Gemma)0.023
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: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.104
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.023
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0090.010
Scholarly communication0.0140.018
Open science0.0020.006
Research integrity0.0060.009
Insufficient payload (model declined to judge)0.1040.061

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 head0.199
GPT teacher head0.364
Teacher spread0.165 · 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicResearch Data Management PracticesFrench-language works237,207