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Record W2495645098 · doi:10.1057/978-1-137-40096-3_9

Changing Sexual Interests, Identities, and Behaviours

2016· book-chapter· en· W2495645098 on OpenAlexaff
James Horley, Jan Clarke

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2016
Typebook-chapter
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsAlgoma UniversityUniversity of Alberta
Fundersnot available
KeywordsConstruct (python library)AsideHuman sexualityPsychologySocial psychologyIdentity (music)SociologyComputer scienceAestheticsGender studies

Abstract

fetched live from OpenAlex

One obvious implication of a theory of human sexuality based on choice is that if something can be chosen it can also be rejected at a later time. The acceptance of a channelized choice, however, means that the rejection of an adopted and employed construct does not mean that the entire system reverts to its state immediately prior to the adoption of the construct. Constructs come and constructs go; they also evolve with system change. As construct system change occurs, there is a very good chance that self-identity, including a sense of the self as a sexual being, is modified. While this may be true for a PCT-based theory of sexual desire, changing sexual desires let alone sexual identity is easier said than done. We may want to alter our desires and sexual engagements but, aside from a sudden and massive alteration in our current system, any change is likely to be slow, difficult, and perhaps more likely to move backward than forward, especially if we attempt it on our own. While epiphanies can and do occur, they are very rare, and long and slow change is more common and often involves help and support, whether through professional or informal helping networks. Though some therapists may believe in the efficacy of their theories and techniques, the change that can occur through formal helping networks and professionals must be seen as slow, gradual, and incremental change. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0040.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.002

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 head0.032
GPT teacher head0.306
Teacher spread0.274 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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