Asset mapping 2.0; contextual, iterative, and virtual mapping for community development
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
We argue a re-appraisal of asset mapping is needed based on revisiting the concept of assets. Asset mapping is useful for inter/trans-disciplinary work involving complex systems: organizations, administrations, governance systems, social-ecological systems, etc. Asset mapping can be an integrative method, allowing a combination of different disciplinary insights and knowledge types; co-defining what is valuable in and for a system. We propose a new version of asset mapping that combines contextual, iterative, and virtual asset mapping in different manners depending on the system and situation. The unpredictable character of co-evolution makes iterative asset mapping important, contextual asset mapping allows different delineations of relevant contexts, and virtual asset mapping entails recognizing assets in different futures, either scenario-based or as strategy options. We argue that this novel approach is particularly important for planning, in the broad sense, because it provides a bridging opportunity with other fields, connecting discourses and policy.
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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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