Bibliographic record
Abstract
Many writers from a range of disciplines have argued that all sexual relationships are first and foremost power relationships. Giddens (1992), for example, described sexuality as a social construct “operating within fields of power, not merely a set of biological promptings which either do or do not find direct release” (p. 23), and Brickell (2009) claimed that power “is intrinsic to sexuality” (p. 57). Following a series of interviews concerning sex within heterosexual relationships, Holland, Ramazanoglu, Sharpe, and Thomson (1998) came to the conclusion that both males and females collude in promoting a single standard of dominant heterosexual masculinity, the “male-in-the-head” (p. 11). Overall, the view that sexuality and power are intertwined is so common that overlooking the nature and effects of social power in sexuality would be very difficult to imagine. Understandings of power relations in different sexual contexts, complex though they may be, are central to comprehending fully various sexual expressions and sexual relationships. These understandings offer important social considerations for an expanded personal construct theory (PCT). Not only do individuals’ constructs require analysis but social factors such as oppression, privilege, social inequalities, social control, and resistance to power also demand attention. This chapter draws on theory and research from various social science disciplines on power relations in general, and sexuality in particular, to consider how power impacts sexuality and how PCT does and should accommodate social power. 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.037 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".