Bibliographic record
Abstract
In a study of novice teachers’ I investigate the conception of randomness among teachers college trainees thatstudy probability and statistics. I gave the students questionnaires which serve to explored their conception andproblem-solving approach with respect to probability problems, conception of randomness and deciding aboutthe randomness of sequences and arrays, solving a THOG type problem, which raise the use of matching biasescharacteristics. I will show that the students use a set of rules to solve these different categories of problems.There is a common heuristic for all or many of these problem solving categories: the problem solver relay onheuristics anchored on the symmetry and asymmetry of sets of objects or more abstract elements to decide onquestions like what is the probability of getting a certain sample in a random drawing of beads, the randomnessof an array; certain sequences are measured by looking at the amount of apparent order in-order to makedecisions concerning randomness and deviation from symmetry of the sample also effect the decision. Thischaracteristic heuristic approach of problem solvers is founded on a set of simple rules relating to order,deviation from symmetry, the distribution of patches in the plane and occurrence of ordered subsequences incertain linear arrangement of sequences.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".