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Record W2894895648 · doi:10.3138/cbmh.237-112017

From Improving Egos to Perfecting Smiles: Orthodontics and Psychology, 1945–2000

2018· article· en· W2894895648 on OpenAlexaffvenueabout
Melissa Micu, Catherine Carstairs

Bibliographic record

VenueCanadian Journal of Health History · 2018
Typearticle
Languageen
FieldPsychology
TopicAcademic and Historical Perspectives in Psychology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsOrthodonticsPsychoanalysisPsychologyHistorySociologyMedicine

Abstract

fetched live from OpenAlex

From World War II to the end of 20th century, the types of patients undergoing orthodontic treatment and their reasons for doing so changed significantly. In the 1950s and 1960s, Canadian parents were told that orthodontics would “cure” inferiority complexes and protect children with crooked teeth, especially girls, from a life of delinquency and missed opportunities. By the last two decades of the 20th century, the consumer health movement and rising incomes empowered patients to decide which treatments were right for them, and an increasing number of adult patients sought orthodontic treatment to improve their appearance. Orthodontists never abandoned their claim that orthodontic treatment could improve psychological health, as while health psychologists and other researchers increasingly called this into question. But orthodontists did begin to place greater emphasis on aesthetics as a reason for treatment, and orthodontics became part of a much larger explosion in “cosmetic dentistry” procedures that came to include tooth whitening and veneers.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.567

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.007
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.058
GPT teacher head0.368
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes3
Has abstractyes

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