Bibliographic record
Abstract
Although we accept that a scientific mosaic is a set of theories and methods accepted and employed by a scientific community, scientific community currently lacks a proper definition in scientonomy. In this paper, I will outline a basic taxonomy for the bearers of a mosaic, i.e. the social agents of scientific change. I begin by differentiating between accidental group and community through the respective absence and presence of a collective intentionality. I then identify two subtypes of community: the epistemic community that has a collective intentionality to know the world, and the non-epistemic community that does not have such a collective intentionality. I note that both epistemic and non-epistemic communities might bear mosaics, but that epistemic communities are the intended social agents of scientific change because their main collective intentionality is to know the world and, in effect, to change their mosaics. I conclude my paper by arguing we are not currently in a position to properly define scientific community per se because of the risk of confusing pseudoscientific communities with scientific communities. However, I propose that we can for now rely on the definition of epistemic community as the proper social agent of scientific change.Suggested Modifications[Sciento-2017-0012]: Accept the following taxonomy of group, accidental group, and community:Group ≡ two or more people who share any characteristic.Accidental group ≡ a group that does not have a collective intentionality.Community ≡ a group that has a collective intentionality. [Sciento-2017-0013]: Provided that the preceding modification [Sciento-2017-0012] is accepted, accept that communities can consist of other communities.[Sciento-2017-0014]: Provided that modification [Sciento-2017-0012] is accepted, accept the following definitions of epistemic community and non-epistemic community as subtypes of community:Epistemic community ≡ a community that has a collective intentionality to know the world.Non-epistemic community ≡ a community that does not have a collective intentionality to know the world.[Sciento-2017-0015]: Provideed that modification [Sciento-2017-0013] and [Sciento-2017-0014] are accepted, accept that a non-epistemic community can consist of epistemic communities.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.013 | 0.029 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".