Training in the solid wood manufacturing industry : a comparison between interior British Columbia, Alberta and New Zealand
Bibliographic record
Abstract
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".