Learning from the retraction and ridicule of fraudulent research about people and pet dogs in urban parks
Bibliographic record
Abstract
Rock, M.J. & Blue G. 2023. The Grievance Hoax, critical scholarship, and healthy cities as multi-species spaces: Learning from the retraction and ridicule of fraudulent research about people and pet dogs in urban parks (Presentation). Resilient Communities: One Urban Health for resilient, inclusive, and innovative cities (Symposium). Organizers and Panelists (in alphabetical order): C. Aenishaenslin (Université de Montréal), M. Boiteux (Université de Montréal), M. Bouchard (Université de Montréal), T. Hancock (University of Victoria), C. Lawand (Health Canada), E. de Leeuw (University of New South Wales), S. Lord (Université de Montréal). Canadian Science Policy Conference, 13-15 November.
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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 | Research integrity Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | MetaresearchResearch integrity Domain: Methods · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".