Introduction to Python's Syntax
Bibliographic record
Abstract
This tutorial is the second in our series and covers basic syntax in Python with examples related to psychology. The aim is to teach programming beginners and experts alike the fundamentals required in order to smooth the learning curve and succeed with integrating Python with their research. It starts by covering basic built-in functions and variable creation. Next, different data types and data structures that you will encounter are covered detail, followed by comments and best commenting practices. Finally, indentation, logic, conditional statements, and loops are all explained with simple, illustrative examples. The tutorial ends with a comprehensive example of the same-different task from cognition that ties together everything learned. With this foundation, the reader will gain the confidence to begin practicing Python on their own and think of ways to incorporate it into their own research and daily lives.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.115 | 0.090 |
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".