Bibliographic record
Abstract
(PRCS). Unlike citizen science and crowdsourcing projects that generate raw materials for product development, PRCS benefits capitalist firms primarily by improving their public image and deflecting accusations of causing harm. Three cases illustrate how PRCS works: (1) a growing assortment of citizen science projects associated with Antarctic tourism, (2) an initiative to document biodiversity, linked to Canada's oil and gas industry, and (3) a study sponsored by Biology Fortified, a nonprofit organization that works to communicate positive information about agricultural biotechnology. Scientists and research organizations may have legitimate reasons for entering into these partnerships, but PRCS can benefit industries in problematic ways. First, by supporting environmental science, PRCS can attach a 'sustainable' image to a polluting industry, without changing its core practices. Second, PRCS can accumulate data and steer volunteers' observations in ways that undermine claims about the harms caused by the industry's practices or products. Finally, in some cases, PRCS organizers hope to induce people to view an industry more 'rationally' than those who make 'emotional' or 'ideological' claims about its harms.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | low |
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 | 0.032 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.019 | 0.045 |
| Scholarly communication | 0.035 | 0.039 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.024 | 0.026 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".