Immigrant Female Workers and Australian Trade Unions
Notice bibliographique
Résumé
One of the main goals of employment equity programmes is to increase the occupational mobility of women so that they may enter non traditional jobs. Although on-the-job training has always been considered a major tool for increasing occupational mobility, it has received very little consideration in employment equity programmes. This article identifies the major aspects of women's participation in on-the-job training programmes and proposes ways to bridge the gap between these programmes and employment equity. The first section of the article examines the current situation of women in the labour market. According to the most recent statistics, occupational segregation as well as the wage gap between males and females are still quite significant. Studies of employment equity programmes show that progress has, in general, been slow. Among the reasons given for these results are a lack of integration of employment equity programmes and employers' human resource management strategies, particularly as regards on-the-job training. In the second section we examine data depicting different aspects of women workers' participation in on-the-job training. The data originate from a wide variety of sources and show that women are disadvantaged relative to men along several dimensions, including: participation rates; duration and intensity of training; and financing. Explanations for these discrepancies are discussed in the following two sections which deal respectively with access to and outcomes of on-the-job training for women workers. An initial factor that may explain the lower participation rate of women is the fact that employers tend to offer on-the-job training to workers in qualified jobs. In this context, women's jobs are usually considered as less qualified and studies show that there is an under-investment in human capital in jobs such as clerical work where women are concentrated. Another factor that comes into play is the relation between on-the-job training and an employee's real or anticipated stability in the enterprise. Although women's periods of absence from the labour market for family reasons are much shorter now than before, they are still perceived as less attached to their jobs. It has been shown, for instance, that women with young children are less likely to be offered training by their employers. Another characteristic of women's jobs that negatively affects their participation in training is the fact that they are over-represented in areas of marginal employment such as part-time work. Employers tend to offer less training to these employees because they are considered less motivated and less productive. Finally, the positive relation that has been shown to exist between size of firm and the incidence of training may equally have an adverse impact since women are over-represented in small and mediumsized firms. Even if employers modify their practices in order to give women access to training for non-traditional jobs, some barriers can still limit positive outcomes. These barriers may exist during or after training. For instance, for reasons arising from education or the traditional nature of their job experience, women are less familiar with basic technical skills. Studies have shown that the chances of women remaining in and successfully completing these programmes are improved if, prior to training, they receive some technical or scientific instruction in areas such as refresher mathematics or technical vocabulary. Positive attitudes and awareness on the part of trainers are also important factors in avoiding discouragement and withdrawal from the programme by women workers. Finally, once training is over, occupational mobility may not necessarily follow for various reasons such as bias of supervisors against women entrants in non traditional jobs or a lack of seniority. The analysis presented in this article proposes a comprehensive framework for studying women's participation in on-the-job training programmes. One of the main features of the analysis is to demonstrate the interactions between a large number of variables. The next step is to improve our knowledge of this situation through use of a more comprehensive statistical data base, and through case studies. Given the fact that hiring has considerably slowed down for cyclical as well as structural reasons, promotions into non-traditional jobs will probably become the main avenue for employment equity, hence the importance of on-the-job training for women workers.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».