Bibliographic record
Abstract
Throughout my entire career the underlying aim has been the under-standing of intimate interpersonal relationships, especially the earliest of these, and how they influence subsequent personality development. Undoubtedly it was this interest—then half-recognized—that led me to choose to study psychology when an undergraduate at the University of Toronto in the early 1930s. This core interest became overt after having attended Professor William Blatz’s courses in genetic and abnormal psy-chology, in which he introduced us to his “security theory. ” At the same time, an experimental project directed by Professor Sperrin Chant taught me that research could be fascinating. These experiences, especially, led me to stay on at Toronto as a graduate student. Theoretical and research interests were happily combined in 1936 when Blatz suggested that I undertake dissertation research relevant to his security theory under his and Chant’s supervision. My dissertation was completed in 1939 and published in the following year (Salter, 1940). I believe that it was the first publication stemming from Blatzian security theory. Blatz and I intended then to assemble a team to continue and expand this research, but the outbreak of war intervened. Blatz became involved in the establishment of wartime nurseries in Great Britain. I remained in the department for 3 years as a member of the faculty, but Au: Numbers measuring time or age are not spelled out but expressed as numerals in APA.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".