Bibliographic record
Abstract
Introduction Dramatic changes have occurred in the scale of investment in employee education in the United States and Canada since the end of World War II. Business and industry leaders in the United States and Canada have recognized that an educated workforce is essential to remaining competitive in a global economy. At the same time, there is widespread dissatisfaction with the knowledge and skills of graduates of the public educational system, which has led to efforts to provide remedial education. In addition, technological advances require reeducation of employees to maintain their competence. Corporations also see employee education programs as a means to reduce costs associated with employee turnover and absenteeism (Allen, 1996). In 1997, organizations in the United States with 100 or more employees were expected to spend almost $60 billion for employee education (Training budgets, 1997). This estimate does not include the costs of informal on-the-job education, nor does it include indirect costs, such as the wages and benefits paid to employees while they are participating in educational programs. Souque (1996) reported that a survey of Canadian organizations found that employers are spending 1.6% of payroll on training and development for their employees. He reported that a recent survey in the United States reported a comparable figure of 1.5%. Davis and Botkin (1994) have argued that business is now becoming more responsible than government for the kind of education that will maintain competitiveness, from the development of basic skills to sophisticated professional development programs. They describe the development of corporate universities and note that although some of these are little more than rechristened training centers, they nonetheless reflect a growing commitment to employee education. Meister (1994) profiled education practices of 30 U. S. corporations that have established universities, including Arthur Andersen, Federal Express, General Electric, and Motorola. She noted that many corporations assist their employees who complete education programs to obtain academic credit toward university degrees. The American Council on Education Program on Noncollegiate Sponsored Instruction (ACE/PONSI) evaluates instructional courses and programs offered by business and industry, labor union s, professional and voluntary associations, and government agencies and makes recommendations for college credit based upon such instruction. ACE/PONSI was initiated in 1974, and today over 250 companies and organizations, including ATT Eurich, 1985; Hawthorne, Libby & Nash, 1983; Nash & Hawthorne, 1987; Porter, 1982). …
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.056 | 0.017 |
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".