The Territorial Intelligence Process: Ecology of Communication for Development of Hybrid Territories
Bibliographic record
Abstract
Aims: This article aims providing structural, and deeper, answers to the words used in titled, how “The Territorial intelligence process can be considered as necessary Ecology of Communication for development of “hybrid territories” and because in first part, we have been working, researching, for more decades in territorial intelligence field and secondly, because we have seen from the TICs development that territorial organizations in 21st Century are becoming hybrid, a mix made of physical (geographical) territory and digital territory and call for appropriate path to think about their future. Study design, Methodology & Place and Duration of Study: We illustrate our arguments by drawing on five situations of specific PhD research conducted in the interval from 2000 to 2014 throughout E.U in general and in France, in particularly. Reflecting the past six years, (2008-2014), they were fueled from the exercise of two local mandates. Results & Conclusion: All aspects encountered during these years need extracting additional points for consideration in the future. In conclusion, we propose structural elements for a response, and perhaps, for a future program of hybrid territories to be developed with the help of territorial intelligence process because of exposed territory to an insular development potentially breaking their continuum.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".