Tracking Science: An Alternative for Those Excluded by Citizen Science
Bibliographic record
Abstract
In response to recent discussion about terminology, we propose “tracking science” as a term that is more inclusive than citizen science. Our suggestion is set against a post-colonial political background and large-scale migrations, in which “citizen” is becoming an increasingly contentious term. As a diverse group of authors from several continents, our priority is to deliberate a term that is all-inclusive, so that it could be adopted by everyone who participates in science or contributes to scientific knowledge, regardless of socio-cultural background. For example, current citizen science terms used for Indigenous knowledge imply that such practitioners belong to a sub-group that is other, and therefore marginalized. Our definition for “tracking science” does not exclude Indigenous peoples and their knowledge contributions and may provide a space for those who currently participate in citizen science, but want to contribute, explore, and/or operate beyond its confinements. Our suggestion is not that of an immediate or complete replacement of terminology, but that the notion of tracking science can be used to complement the practice and discussion of citizen science where it is contextually appropriate or needed. This may provide a breathing space, not only to explore alternative terms, but also to engage in robust, inclusive discussion on what it means to do science or create scientific knowledge. In our view, tracking science serves as a metaphor that applies broadly to the scientific community—from modern theoretical physics to ancient Indigenous knowledge.
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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 | 0.083 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.026 | 0.143 |
| Scholarly communication | 0.030 | 0.075 |
| Open science | 0.007 | 0.037 |
| Research integrity | 0.035 | 0.038 |
| Insufficient payload (model declined to judge) | 0.007 | 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".