Bibliographic record
Abstract
I first became familiar with Dr. David Berliner’s work in the early 1970s, when I was a board member of the Far West Regional Laboratory for Educational Research and Development in San Francisco. I did not know at the time that I would come to regard him as one of the greatest researchers in education. Dr. Berliner was a senior researcher at the laboratory. I was highly impressed then with the work that he and Dr. William Tikunoff were doing on the Beginning Teacher Evaluation Study for the Educational Testing Service. This was one of the earliest studies to look at the distinctive features of successful teacher behavior, as compared to unsuccessful teacher behavior. Berliner and Tikunoff (1976) found 21 behaviors that separated the two groups of teachers. I believed then, as I believe now, that this approach, studying successful teacher behavioral processes or failure outcomes closely, would yield powerful results. This approach can be contrasted with the more typical research that begins from theory of some sort, ignoring the clear fact that some teaching is highly successful. Of course theory very is important, however so many theory first approaches have produced little of power in changing teaching. They have the disadvantage of perpetuating the idea that students are deficient in abilities. They tend to give undue weight to factors associated with poverty.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".