Bibliographic record
Abstract
Classroom and facilities management require more than a series of techniques. Management and safety require a philosophy. Veteran teachers who “make it look easy“ have not perfected the techniques of management inasmuch as they have integrated certain techniques into a system and philosophy of C&I, assessment, discipline, facilities design, and safety. We can think of our combination of techniques and philosophies as flexible superstructure that complements our somewhat inflexible infrastructure of architectural units, devices, software, tools, and machines. The greatest amount of anxiety for new teachers tends to be over classroom management, and specifically the way that individual students are disciplined for incivilities. Rather than confronting incivilities, effective management and safety depends on preventive infrastructure and systems that are in place. This point cannot be stressed enough. Students will test new and veteran teachers alike. Veteran teachers may have the benefit of experience in dealing with incivilities such as bullying, but they rely on their infrastructure and systems of prevention rather than their reactive techniques. They know how to deal with individual incivilities but prefer preventive measures by setting a tone for acceptable classroom behavior. We will explore a range of techniques, including humor, for dealing with classroom behavior.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".