Training in the solid wood manufacturing industry : a comparison between interior British Columbia, Alberta and New Zealand
Notice bibliographique
Résumé
This study examines training in solid wood manufacturing companies in Interior British Columbia, Alberta and New Zealand and determines the success of implementation at the sawmill site level. Information obtained in this study will be used to determine the degree of the company's investment and commitment to training; to determine the types of training programs for management and staff; and to define desired skills set that are required by the company. Two mail surveys were developed for the study. The population for the corporate survey consisted of 17 solid wood manufacturing companies having two or more sawmills while the sample for the site survey consisted of 53 sawmill sites. The response rate was high where 64% and 51% of the sample responded to the corporate and site surveys, respectively, in the three regions. Topics in the survey included training policies, corporate and site budgets, training programs, skills requirements, and training culture. Results of this study show that a majority of companies had informal training policies, training decisions were largely decentralized, and sites were committed to increasing their training budget for the year 1999, where about three-quarters was allocated to specific training. In addition, the companies invested in their in-house trainers and direct supervisors to train their employees and widely used teamwork, on-the-job training, cross training, and retraining programs to expand their employees' skills. This was backed by their immense use of employee performance measures and productivity reports to evaluate training. Although almost all companies strongly viewed training as an investment and encouraged employees to develop new knowledge, they did not have long-term plans to identify their skills needs. Time constraints and financial resources were seen as a major barrier to training their employees. Overall, corporate respondents were not significantly different from site respondents with respect to their attitudes and behaviors towards training. Most sawmill respondents said that site operations improved in the last three years as a result of training. In an effort to improve training within the company, about half of the respondents said they needed to do more effective succession planning, better link training needs to overall business strategies and develop more results-oriented evaluation measures.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».