Topic Group A Implementation of an Apple Centre for Innovation and Year 1 Mathematics Results
Bibliographic record
Abstract
Hubert (1988) estimated nearly 27 000 computers in Alberta schools at the end of 1987. This translates into about a 1:15 computer to student ratio or an average of about 100 minutes per week of computer access for each child in Alberta. The actual time a child spends at a computer, of course, varies significantly from this theoretical average. Questions remain. If children had continuous access to a computer all day, every day, what could they do? What would they learn? Would their thinking patterns change? How would the school program change? The Proposal In an attempt to at least partially answer the broad and open questions stated above, a proposal was submitted to the Apple Canada Education Foundation (ACEF) for the establishment of an Apple Centre for Innovation (ACI) in a third grade classroom. The proposal called for the installation of 1 complete Apple II GS microcomputer workstation for each child in the classroom. The plan was to network the computers and printers and ultimately to incorporate a file server. With respect to the curriculum, the plan was to
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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.090 | 0.018 |
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".