“Don’t look down on us!”
Bibliographic record
Abstract
Purpose Although some research has been done with entertainment workers in KTV bars, little has been done to gain a qualitative understanding of the vulnerabilities of masseuse women. This is due to a prevalent focus on girls under 18 years, thereby excluding most masseuses. The paper aims to discuss these issues. Design/methodology/approach In total, 98 female participants completed an interview answering both open and closed questions concerning relationships, stigma and discrimination and personal sexual abuse among other topics. Findings This study finds that many women working in the massage parlor industry are vulnerable to various threats, including sexual exploitation and abuse. Dependency structures within the massage parlor as well as with their families seem to play a key role in the life situations that can be positive as well as negative, and in some cases both. Social implications The findings provide a baseline evaluation of the vulnerable conditions of the massage industry for women in Cambodia. Specifically, that stigma, discrimination, physical and sexual abuse, as well as a lack of access to education are all vulnerabilities not commonly covered, especially with adult women working in the entertainment industry. This research has been adapted from the original transcript “Strive, No Matter What” (Miles et al., 2014). Originality/value The paper seeks to understand the reality of life and vulnerabilities as perceived by Phnom Penh’s female masseuses in the lower-priced massage parlors which is largely unknown.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".