Bibliographic record
Abstract
a bibliophile and text addict, who inhaled words and revelled in the worlds thus created iii ivPreface T his book comprises a collection of lecture notes for the statistics component of the course Psychology 2030: Methods and Statistics from the Department of Psychology at the University of Lethbridge. In addition to basic statistical methods, the book includes discussion of many other useful topics. For example, it has a section on writing in APA format (see Chapter 17), and another on how to read the professional psychological literature (see Chapter 16). We even provide a subsection on the secret to living to be 100 years of age (see section C.2.2)—although the solution may not be fully satisfactory! Despite this volume comprising the fourth edition of the book, it is still very much a work in progress, and is by no means complete. However, despite its current limitations, we expect that students will find it to be a useful adjunct to the lectures. We welcome any suggestions on additions and improvements to the book, and, of course, the report of any typos and other errors. 1 Please email any such errors or corrections 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.543 | 0.525 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".