Forest Products Technician Programs in The United States and Canada: An Swst Education Committee Report
Bibliographic record
Abstract
The objectives of this report are twofold: 1) to furnish, for the first time in one report.a summary of information on forest products technician programs in the United States and Canada to students, faculty members, and employers; and 2) to promote, at least on the regional level, coordination of these programs with 4year professional programs offered by the 25 colleges and universities in the United States and Canada.College placement officers note two major trends today: an increasing demand for (a) graduates having B.S. degrees in forest products with minors in business management, and (b) graduates trained as forest products technicians.This demand for products technicians promises to increase as the forest industries utilize the wealth of technological innovations now available to them.Mechanization, automation, and increased efficiency within the forest industries typify the changes that face these technicians.Such changes will demand more ability and training on the part of these people (Loudermilk 1966).However, vocational and technical institutions tend to lag behind industrial needs (Coba and lrvin 1975).Advisors of students are often aware that their advisees would be very successful as forest products technicians, but do not have the interest, academic preparation, andlor ability to undertake a 4-year professional university program in forest products.At this time, prospective students are showing more and more interest in forest-related courses such as sawmilling and woodhandling (Ringling 1978).This report will help the student and advisor make the right choice in selecting an educational program to follow.It will also aid employers in locating and hiring well-trained forest products technicians.The following references were used to compile a list of colleges offering technician programs in forest products: Barron's Guide to the Two-Year Colleges ( 1978) and The College Blue Book (1977).Using this list, we contacted the 233 American and 12 Canadian colleges that were reported to offer technician programs in forestry.Of the 153 colleges that replied to our inquiry, 54 colleges (49 American and 5 Canadian) offer programs in forest management only and 91 have no forestry programs.We found only eight colleges (7 American and 1 Canadian) offering technician programs in forest products andlor pulp and paper.These programs are described in Table 1.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".