Innovation Networks: A Tool for Food-Culture Preservation and Sustainability in the Era of Globalization
Bibliographic record
Abstract
The growing exposure to globalization, since 1990s, has initiated some significant alterations to the Lebanese economy, society, and culture. For the last two decades, it has been observed that international cuisines and eccentric menu items have been invading the local market and taking over ethnic and traditional cuisines, what threatens, if this trend continues, the identity of traditional cuisine and, consequently, the sustainability of local food culture. Departing from the case of Lebanon, this paper studies the impact of globalization on traditional cuisine and highlights the role of networks in sustaining local food culture. The findings of our empirical study revealed the necessity to modernize the traditional cuisine through a coordinated set of heterogeneous and professional actors who collectively take part in the process. The ability of these actors to innovate is found related to the organizational conditions of the networks to which they belong, and to the ability of these networks for innovation, what refers us to the concept of “innovation network” that we are proposing, through this study, as a solution to the dilemma of food - culture preservation and sustainability.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".