Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this paper is to develop propositions for empirical validation regarding appropriate management planning and control systems (MPACS) in knowledge‐intensive organizations. Design/methodology/approach The propositions were developed from interviews with members of a knowledge‐intensive virtual organization that is known for its innovative practices regarding intellectual capital (IC) development and surveys from low to middle range managers, using a semi‐structured questionnaire, from a variety of companies. Trends in responses permitted us to identify issues of importance in developing innovative MPACS for knowledge‐intensive companies. Findings The paper proposes that two variables, the level of IC intensity and the uncertainty of knowledge, are important for determining the degree of adaptive versus generative characteristics that an organization's MPACS should contain. Regarding IC, the paper further proposes that organizations must give careful thought to ensure that both adaptive and generative characteristics are aligned with four MPACS elements of focus, commitment, capability, and learning. Originality/value As organizations develop programs to realize the potential from their intellectual capital, many fail to develop MPACS that are appropriate for knowledge‐intensive environments. MPACS should support knowledge creation, as well as knowledge sharing, and contain elements of both adaptive and generative systems.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it