Bibliographic record
Abstract
This article presents the “Stra.Tech.Man approach” (strategy-technology-management synthesis) as the basis for creating a “Stra.Tech.Man Scorecard,” which can be used for the strategic audit of every socio-economic organization. After reviewing the literature on strategic control and strategic audit, the study proceeds with a critical appraisal of Kaplan and Norton’s balanced scorecard model and presents the theoretical foundations of the Stra.Tech.Man approach. It composes a first conceptual outline of the Stra.Tech.Man Scorecard, which can function as an integrated monitoring tool, exploring and describing the evolution of “physiologies” of the studied socio-economic organizations (firms). It concludes that the formal balanced scorecard approach: (a) has been applied mainly to larger and more sophisticated organizations, (b) does not offer a compound understanding of the central dimensions of strategy, technology, and management that can be linked in an integrated way to the financial results, (c) leaves relatively unspecified many critical aspects of a firm’s choices, especially in strategy articulation, technology selection, and management implementation, (d) does not create complete profiles for the firms’ evolutionary physiologies. In contrast, the Stra.Tech.Man Scorecard: (i) does not have as a prerequisite any pre-existing systematic performance measurement framework in the organization and, therefore, it is not limited by any firm size, type, or physiology, (ii) it links in an evolutionary way the “core” qualitative dimensions of strategy, technology and management (Stra.Tech.Man audit) with the quantitative financial results of the organization, (iii) it can and has been used as an integrated analysis instrument by taking into account more adequately the evolutionary dimensions of the meso-environment of organizations besides the micro-level of analysis which the balanced scorecard is primarily associated.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".