Bringing the School Back to School Research: Toward an Integrated Organizational Sociology of Education
Bibliographic record
Abstract
School research has been dominated by perspectives focused on human capital and program effects; what has receded is the organizational perspective highlighting the school bureaucracy, teaching profession, conflicting actors, and socialization of students. However, this loss of an organizational perspective runs the risk of studies that are deterministic, individualistic, and ahistoric. Highlighting the “organization” as both entity and process, this essay integrates concepts for an organizational sociology of education. First, I synthesize theoretically dispersed studies on school leadership, policies, and processes with the concept of school structures as the organization of relationships, resources, and information—consequential for instructional effectiveness, support, and change. Next, I suggest that networks actualize these structures through interpretation and interaction, and through leveraging social and cultural capital. Then, I argue how ecologies of schools, educational bureaucracies, and school improvement industries drive inequalities, innovations, and institutionalized practices. I conclude with how this integrated perspective provides synthesized schemas for key topics in the sociology of education and reveals gaps for further research.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.006 | 0.057 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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".