Bibliographic record
Abstract
The concept of coupling—the relationship between the environment, administrative goals, and instructional practices of education organizations—is a staple in New Institutional research. Yet processes of coupling have remained elusive. Drawing on ethnographic research of the ‘‘Ontario Learning Center’’ (OLC) franchise, along with interviews of franchise owners and representatives, this article examines an ideal-type, tightly coupled organization. Despite the fact that the educational materials, the progress monitoring, and even the ‘‘emotional labor’’ of instruction are highly formalized and monitored, the author discovered evidence of loose coupling everywhere. Most strikingly, loose coupling is being accomplished in the context of rule following (rather than rule breaking) based on how managers and instructors interpret and prioritize available technical and institutional frameworks. By examining these processes, this article makes two contributions: First, it examines the symbolic dimensions of tightly coupled organizations by articulating how organizations and their actors reinterpret environmental demands in mutually beneficial ways. Second, this article situates the growing ‘‘inhabited institutions’’ literature within the new realities of education organizations, examining how meaning is actively constructed and how such processes generate understandings about appropriate lines of action.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".