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
The origins of SWOT analysis have been enigmatic, until now. With archival research, interviews with experts and a review of the available literature, this paper reconstructs the original SOFT/SWOT approach, and draws potential implications. During a firm's planning process, all managers are asked to write down 8 to 10 key planning issues faced by their units. Each manager grades, with evidence, these issues as either safeguarding the Satisfactory; opening Opportunities; fixing Faults; or thwarting Threats: hence SOFT (which is later merely relabeled to Strengths, Weaknesses, Opportunities and Threats, or SWOT). Subgroups of managers have several dialogues about these issues with the instruction to include the needs and expectations of all the firm's stakeholders. Their developed resolutions or proposals become input for the executive planning committee to articulate corporate purpose(s) and strategies. SWOT's originator, Robert Franklin Stewart, emphasized the crucial role that creativity plays in the planning process. The SOFT/SWOT approach curbs mere top-down strategy making to the benefit of strategy alignment and implementation; Introducing digital means to parts of SWOT's original participative, long-range planning process, as suggested herein, could boost the effectiveness of organizational strategizing, communication and learning. Archival research into the deployment of SOFT/SWOT in practice is needed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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 | 0.000 |
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 it