Plotting Directionality on Positional Maps: A Methodological Consideration for Situational Analysis
Bibliographic record
Abstract
In this article, we aim to expand situational analysis (SA), oriented by complex adaptive systems (CAS), by adding the dimension of directionality over time to positional maps. This addition furthers the analytic utility of SA and can aid researchers in identifying areas for transformative action regarding social justice and health equity issues. Adding directionality over time to positional maps pushes researchers to explore how positions move, evolve, and how they could continue to develop. Analyzing these elements expands the analytic utility of positional maps as researchers abductively analyze explicit connections between theorized antecedents, current conditions, and potential futures within a CAS to understand positional movements. The purpose of this analysis is not as a predictive tool but as a tool in identifying potential actionable areas for interventions while further grounding SA in its Foucauldian and Straussian theoretical roots. We use an ongoing public health project in the Ngorongoro Conservation Area, Tanzania, to demonstrate how a researcher can apply directionality over time to positional maps.
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How this classification was reachedexpand
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 | 0.065 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".