Bibliographic record
Abstract
Computational thinking is an approach to problem solving that is typically employed by computer programmers. The advantage of this approach is that solutions can be generated through algorithms that can be implemented as computer code. Although computational thinking has historically been a skill that is exclusively taught within computer science, there has been a more recent movement to introduce these skills within other disciplines. Psychology is an excellent example of a discipline that would benefit from computational thinking skills because of the nature of questions that are typically asked within the discipline. However, there has not been a formal curriculum proposed to teach computational thinking within psychology and the behavioural sciences. I will argue that computational thinking is a fundamental skill that can easily be introduced to psychology students throughout their undergraduate education. This would provide students with the skills necessary to become successful researchers, and would also provide a practical and marketable skill to all psychology graduates.
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 | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.007 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".