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Record W3033595895 · doi:10.14515/monitoring.2018.6.01

Social transformation of gender and sexuality as viewed by I.S. Kon

2018· article· en· W3033595895 on OpenAlex
Irina Tartakovskaya, Igor I. Lunin

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMonitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHuman sexualityTransformation (genetics)Social transformationGender studiesSociologyPsychologySocial psychologySocial changePolitical scienceBiologyGenetics

Abstract

fetched live from OpenAlex

The article examines the influence of Igor Kon on many aspects of modern research in the field of gender and sexuality. The authors conclude that it was Igor Kon who identified several key trends describing the current state of gender order in sexuality, namely, individualization and pluralization of cultures and lifestyles. It seems that the better way to speak about sexuality is not to refer to single or “normative models” but about a set of sexualities. In the proposed work, the variety of combinations of different gender identities with a multitude of sexual preferences is shown on the example of the theory of sexual configurations proposed by Canadian researcher Sari van Anders.
 The article emphasizes that, as I. Kon warned, it is gender relations and sexuality that are currently at the center of the agenda, since many more serious social problems are extrapolated to them. Cultural and, in particular, gender diversity is perceived by many people as the threat of losing the most basic landmarks in an unpredictable and changeable world. The article provides examples of different types of public policy in relation to binary and non-binary gender categories in different countries of Europe, Asia and North America.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.076
GPT teacher head0.369
Teacher spread0.293 · 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