Sustainability and Productivity Indicators with Sensitivity Truth Table for Unskilled Thai Labour Reverse Migration
Bibliographic record
Abstract
Thailand, a developing country, had labours migrating from the agriculture into the industrial due to higher pay in the past. However the economic force has made the government policy to focus on creativity and developing technology towards automatic production. Unskilled Thai labours are facing a big challenge after retirement, which is called reverse migration. This is a rapidly increasing trend in the near future. The resolving of reverse migration problem can be done by developing unskilled Thai labour through the knowledge management concept. The workplace learning is in coordination with the sufficiency economy philosophy. The key development is for unskilled labours to attend the sufficient knowledge worker activities and become knowledge workers who have self-immunity is known as a “Sufficient knowledge worker”. Due to the evaluation of sufficient knowledge worker as social science being complex, the researcher has to indicate that the assessment of sufficient knowledge worker can be achieved by validating the result of the two major dimensions. The research was conducted in an industrial zone in Lamphun, Thailand. Data collection was collected from 32 samples. The result of knowledge and learning dimension was made in transcript from an in-depth interview using Bloom’s taxonomy. Upon completing the study, the results had been analysed by using the sufficient knowledge workers truth table that relies on the sensitivity truth table concept. The results of the sufficient knowledge workers truth table have sensitivity, specificity, positive predictive value, negative predictive value and a research methodology effectiveness of 92.86%, 0%, 0.96, 0.00 and 89.66% respectively. The outcome represents the improvement of sample group in both high level dimensions. Accordingly, they can manage their financial concern with saving and have their own reverse migration plan. Therefore, the idea enables the workers to have confident in their security in the working life.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".