Turkmen women’s traditional craft skills in post-disaster recovery: the case of the 2019 Northeast floods in Iran
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This study investigates why Turkmen women’s traditional handicraft skills have declined and explains how the local, traditional craft skills accelerated the post-flood recovery of Turkmen women in the aftermath of the 2019 Northeast floods in Iran. Design/methodology/approach The research adopts a case study approach, employing reflective thematic analysis. Findings Post-disaster recovery spurred a shift from traditional to modern lifestyles through new housing designs, enhanced female literacy and greater economic participation. However, this transition devalued traditional crafts due to heightened household chores, material scarcity and reduced market demand. Nonetheless, women with craft skills played a pivotal role in household recovery by repairing damaged items and crafting dowries for their daughters, illustrating their contribution to social and economic resilience. Social implications These research findings shed light on the importance of traditional craft skills in enabling the female household member, in particular, to recover from disasters and contribute to the recovery of their households and communities. Originality/value The originality of this study lies in its focus on the specific context of Turkmen women’s traditional craft skills and their role in post-disaster recovery, particularly after the 2019 Northeast floods in Iran. While there is existing research on post-disaster recovery mechanisms, this study uniquely examines the under-researched impact of traditional craft skills on the recovery process, specifically for female household members.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it