Collective Regulation and Working Women in New Zealand and Fiji
Bibliographic record
Abstract
In many nations, women’s labour market presence is significant though it tends to lag that of men on most indices, including pay and seniority. The ramifications of such are huge for women’s experience of employment, their circumstances in other spheres, and thus societal progress. Employment relations (ER) regulation seeks to structure equitable access to and progress within the labour market. However, despite on-going, work-related gender inequities, there is a relative dearth of cross-national (particularly South Pacific), gender-focused analyses. This constrains the development of theory and policy geared towards gender equality in the labour market. This paper thus compares how recent collective ER regulatory initiatives have been applied in New Zealand (NZ) and Fiji, amid shared and unique national and international conditions. Martin and Bamber’s (2004, 2005) ER system model frames an analysis of qualitative survey responses and documentary evidence to more particularly assess the meaning of ER regulation for working women. It emerges that the achievement of gender equality via regulatory instruments has proven elusive, particularly in Fiji. A withering of formal employment regulation and its decentralisation in NZ; weakly implemented regulation in Fiji’s politically tumultuous setting; and the space left by a shift away from collective bargaining in both contexts has not been replaced by social dialogue, fundamental social rights, and in Fiji, regulatory enforcement. This has contributed to deteriorating circumstances for some working women in NZ and comparatively more in Fiji. However, informants showed a preference for certain regulatory measures for improving working women’s situation, concurring that stronger formal regulation of ER, tripartism and effective enforcement are needed in both nations, and that particular issues for working women require tailored responses. The paper discusses how ER regulatory measures might be theorised in terms of improving working women’s context-bound circumstances in Fiji and NZ, and with regard to the infusion of gender-related and socio-cultural values in wider economic and political approaches. It also examines how regulatory initiatives might operate and impact in a more gender-sensitive way by being re-couched in ‘win-win’, inter-connected terms for different ER stakeholders. For Fiji, much hope also rests on its return to democratic rule, and for both countries, on strengthened union activity.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
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".