The Role of Information and Communication Technology (ICT) in Iranian Olive Industrial Cluster
Bibliographic record
Abstract
The cluster development method is one of the successful developing methods in agricultural industries sectionand small industries, which is based on communication and creation cooperation network between clusterbeneficiaries by increasing the social capital. In this research the data were collected by using census two stagesurvey from olive industrial units in mentioned geographical zone(North Iran) in 2007. The results of thisresearch showed that the social capital in some of beneficiaries groups is weak and brittle, but because of theirstatements there is background of cooperation and increase of their social capital. After recognition andstrengthening the effective factors on increase of social capital, can prepare the background for developing ofnew connective and informative technologies. Naturally economic discompetition of units, diseconomies of scale,negative competition in industry and the other problems which are investigated in the olive industrial cluster isresulting from the lack of information and connection between cluster actives. The aim of industrial clustermethod is increasing the cooperation network between cluster members by increasing the communication andinformation which by increase in using information and communication technology will fallow social capital andthen industrial cluster development method.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".