Bibliographic record
Abstract
Deferential differentiation occurs when the curriculum modification process defers to students’ preferred ways of learning rather than relying on teachers’ judgments. The preferences of 416 students identified as gifted (grades 3-8) for features of differentiated curriculum recommended for gifted students were compared with those of 230 students not identified as gifted. While thinking of their favorite school subject, they responded to the 110 items on the Possibilities for Learning survey. Most and least popular items are reported in nine thematic categories (pace, collaborative learning, choice, curriculum content, evaluation, open-ended activities, expert knowledge, teacher/student relationship, and sharing learning). Self-pacing, choice of topic, and choice of workmates were most popular with students in both groups. Compared with nonidentified students, more of the students identified as gifted wanted to learn about complex, extracurricular topics and authentic, sophisticated knowledge and interconnections among ideas; to work with others some of the time; and to choose the format of the products of their learning. More students identified as gifted also disliked waiting for the rest of the class and asking for help. Overall, the groups’ preferences differed in degree rather than kind, and reflected cognitive abilities frequently cited as distinguishing characteristics of learners with high ability.
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 | 0.004 | 0.031 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".