From security to attachment : Mary Ainsworth's contribution to attachment theory
Bibliographic record
Abstract
Even though John Bowlby (1907-1990) is generally regarded as the founder of attachment theory, Mary Ainsworth’s (1913-1999) contribution is considerable and goes beyond the design of the Strange Situation Procedure and the introduction of maternal sensitivity as decisive for a secure attachment relationship. Ainsworth worked in Toronto with William Blatz (1895-1964) for almost two decades before she moved to London and worked with Bowlby in 1950. Ainsworth was heavily influenced by Blatz and his security theory and infused Bowlby’s attachment theory in the making with elements of Blatz’s security theory. These elements, like for instance the secure base phenomenon, are clearly recognizable even now. The Strange Situation Procedure, an instrument Ainsworth designed to measure the quality of attachment in young children, can also be traced back to her time with Blatz: in the 1930s she designed instruments to measure the concept of security. The Strange Situation Procedure, however, was not the first of its kind: since the 1930s researchers had been experimenting with children, alone or in the company of their parents in unfamiliar surroundings, sometimes in the presence of a stranger. Taken together, we conclude that Ainsworth’s contribution to attachment theory is more significant than hitherto believed.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".