The Struggle for Preventative and Early Detection Networking: The âAsabiyya-Driven Structuration of Womenâs Breast Cancer in the Arab Region
Bibliographic record
Abstract
By 2020, cancer mortality rates are estimated to increase by 180% in Arab countries, where breast cancer is the most common type of cancer. This thesis explores and evaluates the ‘asabiyya-driven structuration (the cohesive force of the group that gives it strength in facing its struggles for progressive reproduction) of cancer agents, government agents, and the World Health Organization agents for breast cancer prevention and early detection in the Arab region. The layers of the philosophical standing from Ibn Khaldûn’s concept of ‘asabiyya and the theoretical foundation of social systems theory, structuration theory, social network analysis, and social capital theory are peeled in order to explore and evaluate the context, constraints, social networks, autopoiesis, and social capital. Utilizing a qualitative research design, this thesis employs content analysis and in-depth interviews, as well as NVivo as a tool for analysis. Data is collected from 122 publications and knowledgeable informants employed by cancer agencies, ministries of health, and World Health Organization offices in Egypt, Jordan, Morocco, and Oman. The findings are divided into the contextual scope of responsibility and resources, the progressive and hierarchal constraining structure, the optimal and weak social networks, the strong and vulnerable shields of autopoiesis, and the presence and absence of social capital momentum, followed by a discussion on the the struggle for structuration against breast cancer. The findings demonstrate that countries with a national cancer control program witness local strengthening ‘asabiyya and ‘asabiyya-driven structuration, while those without a national cancer control program witness weakening local ‘asabiyya. Ultimately, this thesis proposes strategic recommendations to accelerate the regional ‘asabiyya-driven structuration of breast cancer.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".