Should You Pay for the Chicken When You Can Get It for Free? No Longer Life on the Farm as We Know It
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.104 | 0.061 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".