Bibliographic record
Abstract
This paper examines the problem of how institutions and the phenomena called formal or large-scale organization exist—the problem of the ontology of organizations and institutions. It addresses this problem using an approach that has been developed as part of a sociology exploring the social from women's standpoint, from which standpoint the extra-locality and objectification of these forms of organization are problematized. For the most part, sociology formulates the phenomena of organizations and institutions in lexical forms of organization, institution, information, communication and the like, which suppress the presence of subjects and the local practices that produce the extra-local and objective. This paper argues that texts (or documents) are essential to the objectification of organizations and institutions and to how they exist as such. It suggests that exploring how texts mediate, regulate and authorize people's activities expands the scope of ethnographic method beyond the limits of observation; texts are to be seen as they enter into people's local practices of wrking, drawing, reading, looking and so on. They must be examined as they coordinate people's activities.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.059 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".