Sci‐Hub: The new and ultimate disruptor? View from the front
Bibliographic record
Abstract
The Harbinger project was a 3‐year‐long international study of the changing attitudes and behaviours of early career researchers (ECRs). One of the aims of the project was to discover if ECRs were adopting disrupting platforms that, legitimately or illegitimately, promote openness and sharing. It has been alleged that such an adoption appeals to them as Millennials. More than 100 ECRs from seven countries were questioned annually, and questions about Sc‐Hub were raised as part of discussions about discovery and access. Interview data were supplemented by desk research and Google Trends statistics. It was found that Sci‐Hub use was increasing and that a quarter of the ECRs now use it, with French ECRs being the biggest users. However, Sci‐Hub is making little headway with ECRs from the UK, USA, Malaysia, and China, although in China's case, this can be explained by it being banned and the country having its own equivalent, www.91lib.com . Sci‐Hub is used as much for convenience as necessity; use is not connected to the strength of library provision and and it has been suggested that it represents a bigger threat to publishers than ResearchGate, whose star might be waning.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.027 | 0.020 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".