Motivational Design Quality, Internal Benchmarking and Statistical Analysis of Corporate Information Materials: A Pilot Study.
Bibliographic record
Abstract
This paper reports on an exploratory pilot study at Network South Enterprises (NSE) (Manitoba), a non-profit, community-based organization with the purpose of providing employment services for adults with mental disabilities. The study used a focus group, an open-ended questionnaire, and a 36-item InfoMMS scale to determine the motivational appeal of NSE print materials and contents. Seven employers took part in the pre-benchmarking phase of the study to determine any difficulties with the current performance levels of NSE corporate information materials. Another seven employers were later used to determine the performance levels and the effects of newly developed treatment materials and contents. The study provided vital internal pre-benchmarking stage pretest data for: (1) estimating the reliability of the InfoMMS administration; (2) estimating the performance levels or usefulness of existing corporate materials; (3) clarifying design, development, and use of future corporate information materials and preferences; and (4) providing a ready bank of baseline control data against which future materials and associated benchmarking partner data can be compared. (MES) Reproductions supplied by EDRS are the best that can be made from the original document. MOTIVATIONAL DESIGN QUALITY, INTERNAL BENCHMARKING AND STATISTICAL ANALYSIS OF CORPORATE INFORMATION MATERIALS: A PILOT STUDY
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".