Bibliographic record
Abstract
In 2015, the Open Science Collaboration reported in the journal Science that a disturbingly large proportion of psychological studies cannot be replicated (Open Science Collaboration, 2015). The ensuing ‘reproducibility crisis’ became a lightning rod for contesting what counts as legitimate research, and for negotiating the relationship between communication infrastructures and research practice. In the psychological and cognitive sciences, the Open Science community has advocated widespread reforms to incentivize transparency, encourage replication, and detect and discourage questionable research practices. The model of ‘openness’ underlying mainstream Open Science centers on sharing information to increase science’s self-correcting capacity. Against the backdrop of broad-scale transformations in Open Science, this case study depicts how scientists read. By examining the activity of a group of researchers ‘virtually witnessing’ an experiment together, this study reveals reading as a non-trivial process that matters for how research is apprehended and for how science is moved through time and space. The case complicates a disembodied, information-centric ‘openness’ pursued by mainstream Open Science reforms and advocates integrating situated and embodied resources into methods reforms, beginning with practices of reading.
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 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 | 0.028 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.025 | 0.027 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".