Human Capital and Structural Upheaval: A Study of Manufacturing Firms in the West Bank
Bibliographic record
Abstract
Research on small business in environments experiencing radical administrative change and social upheaval has been scarce. A case in point is the West Bank and Gaza of the newly-emerging Palestinian Authority. The characteristics of the owners of small manufacturing businesses in Ramalla (on the West Bank) that have undergone radical and political economic upheaval are examined, focusing particularly on characteristics that influence and assist an entrepreneur's resource allocation and decision-making processes. Data is collected from field interviews with 64 small and micro enterprises over a six-month period, targeting the following variables: business type, resources, performance, company age, size, and characteristics of the owners (i.e., educational levels). Human capital theory is used as a framework for assessing the response of the owners to relocate resources under both pre- and post-intifada West Bank territories. The findings suggest that owners' human capital impacts profitability only in the micro firms studied (with three or fewer employees). Plant capital (location, equipment) is associated only with the profitability of larger SMEs. To explain the reduced importance of human capital and experience in environments of radical transition like Ramalla (specifically, the arbitrary nature and lack of predictability of transitional governments, along with the greater importance of financial capital only to larger SMEs), a model of analysis is proposed. According to this model, skills acquired in functional expertise do not necessarily prepare an entrepreneur for the vicissitudes of transitional environments. Business owners, however, are in a better position to use their cognitive skills in small organizations. Smaller firms experiencing rapid environment upheaval will benefit most from education, training, and advice, whereas larger firms will benefit most from loans providing traditional capital support and advice across the entire firm’s human capital base.Efforts to support an environment like Ramalla should consider firm size before designing and implementing programs of assistance.(CBS)
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".