Multisource assessment programs in organizations: An insider's perspective
Bibliographic record
Abstract
Abstract This study is an overview of multisource assessment (MSA) practices in organizations. As a performance evaluation process, MSA can take various forms and can be complex for an organization to use. Although the literature on MSA is extensive, little information exists on how these programs are perceived by the individuals responsible for their implementation and maintenance. The purpose of this study was twofold: to describe the current MSA practices used in organizations and to assess the issues associated with implementation and management of these practices from the perspective of the individual responsible for managing an MSA program. One hundred one companies located in Canada were surveyed for the study; almost half of these organizations (43 percent) were using MSA. Interviews of managers responsible for MSA in various organizations and some archival data on these organizations were the main source of data for the study. The study revealed that the use of MSA differs widely from one company to another. In addition, results show that, once implemented, MSA requires a number of adjustments. The source of these adjustments centered on employee resistance, lack of strategic purpose for MSA, poor design of the instrument, and problems with the technology used to support MSA. These results are discussed and a proposed research agenda is outlined.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".