From security to attachment : Mary Ainsworth's contribution to attachment theory
Bibliographic record
Abstract
<p>Even though John Bowlby \n(1907-1990) is generally regarded as the founder of attachment theory, \nMary Ainsworth’s (1913-1999) contribution is considerable and goes \nbeyond the design of the Strange Situation Procedure and the \nintroduction of maternal sensitivity as decisive for a secure attachment\n relationship. Ainsworth worked in Toronto with William Blatz \n(1895-1964) for almost two decades before she moved to London and worked\n with Bowlby in 1950. Ainsworth was heavily influenced by Blatz and his \nsecurity theory and infused Bowlby’s attachment theory in the making \nwith elements of Blatz’s security theory. These elements, like for \ninstance the secure base phenomenon, are clearly recognizable even now. \nThe Strange Situation Procedure, an instrument Ainsworth designed to \nmeasure the quality of attachment in young children, can also be traced \nback to her time with Blatz: in the 1930s she designed instruments to \nmeasure the concept of security. The Strange Situation Procedure, \nhowever, was not the first of its kind: since the 1930s researchers had \nbeen experimenting with children, alone or in the company of their \nparents in unfamiliar surroundings, sometimes in the presence of a \nstranger. Taken together, we conclude that Ainsworth’s contribution to \nattachment theory is more significant than hitherto believed.<br></p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".