Bridging disciplines: the natural resource management kaleidoscope for understanding ICTs.
Bibliographic record
Abstract
The potential of information and communication technologies (ICTs) as tools to enhance the development of rural and remote regions remains largely undetermined. The component technologies are designed as commercial tools for industrialized settings and the fact that they have potential for rural and remote community development worldwide is an add-on. The role and impact of the new technology is so vast that a multidisciplinary approach is needed to appreciate it. There are a growing number of tools and diagrams in the literature to capture the multiple dimensions of ICTs, and they all seem to fall short of capturing their very essence. In other words there is a need for a new epistemology to guide this process. This paper provides elements for that epistemology from the field of natural resource management (NRM). The fields of natural resource management and information and communication technology for rural development share several features: they involve multiple dimensions and technical disciplines, multiple stakeholders are involved, a seemingly endless number of variables and indicators need attention, and there is increasing unpredictability and complexity. Four pillars are proposed towards a new epistemology to understand ICTs as tools for rural and remote community development: acknowledging diversity in paradigms; embracing pluralism; embracing a systems approach; and emphasizing learning and participation. The paper describes ongoing action research with attention to stakeholder engagement in planning, tracking impact, and creating local capacities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".