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Record W4378533275 · doi:10.1111/criq.12725

Stress: A Keyword for Today?

2023· article· en· W4378533275 on OpenAlexaboutno aff
Jonathan Arac

Bibliographic record

VenueCritical Quarterly · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth, psychology, and well-being
Canadian institutionsnot available
Fundersnot available
KeywordsCitationComputer scienceInformation retrievalWorld Wide Web

Abstract

fetched live from OpenAlex

Stress: A Keyword for Today?Stress may not prove a keyword, but it emphatically colors our current moment and the last half century.Take as a landmark the psychiatric legitimation in 1980 of post-traumatic stress disorder.Stress has a range of quite precise meanings and usages, but its burgeoning usage comes from all the ways it links mind and body, in reciprocal discomfort.Stress may provide the successor to what W. H. Auden in 1947 termed The Age of Anxiety, but I have not yet encountered a comparably powerful cultural, let alone literary, marker.As I began work for this investigation, I found in my daily online newspaper reading [Headline]: 'Flight cancellations stressing weary travelers as July 4 approaches' (Washington Post, June 28, 2022).In a New York Times 'Mind' feature, 'Stress might age the immune system, new study finds' (June 17, 2022), and in the 'Well' feature, 'Why Dogs Can Be So Healing for Kids: A new study suggests that spending time with therapy dogs may help lower children's stress levels even more than relaxation exercises'.This random chrestomathy illustrates the term's flexible grammar, functioning as an active verb, noun, and adjective.The nominalized adjective formed from the verb features in a fine OED citation from the National Post: 'Headlines tout vitamin drips as a cure-all for the stressed, the anxious, the depressed, the dehydrated, the immune-weakened and the overweight' (Canada, 2015).A striking biographical anecdote concerns the physiologist Hans Selye, whose work had a huge impact on the word's twentieth-century course.As a medical student in the 1920s, Selye observed during ward rounds that patients often had numerous complaints in common, even though suffering from different and distinct diseases.Medical science taught that signs and symptoms are specific to a particular illness; Selye recounted how one of his teachers would make the correct diagnosis in each of five different patients, solely on the basis of their presenting history and physical findings.Ignored, however, were the generic complaints that all those patients had in common, such as feeling tired, having no appetite, losing weight, preferring to lie down rather than stand, and not being in the mood to go to work.He called it the

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0070.012
Open science0.0010.003
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0380.020

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 head0.086
GPT teacher head0.502
Teacher spread0.416 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations0
Published2023
Admission routes1
Has abstractyes

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