Local economic development projects in the Amathole District Municipality
Bibliographic record
Abstract
The global economy has been reeling from the continued effects of the economic crisis since 2007. A range of approaches to economic recovery have been followed, ranging from financial bailout during the 2007/08 financial crisis, to austerity measures in the most recent 2011/12 sovereign debt crisis, but each with limited success. South Africa has similarly experienced significant shockwaves from the meltdown. The South African economy officially entered into recession in the second quarter of 2009. The economy was quick to emerge from economic recession by the first quarter of 2010, but has been on a bumpy path of recovery since. Moreover, economic recovery has been thwarted by the ensuing sovereign debt crisis in the Euro. South Africa is characterised by inequitable growth and development, a high incidence of poverty, a relatively underdeveloped economic base, low levels of skills development and low levels of access to basic services and infrastructure. LED has had a difficult birth in South Africa with regards to accomplishing its objectives of job creation and poverty alleviation. In an attempt to address these problems, the Amathole District Municipality has implemented a number of local economic development projects within the area aimed at improving the wellbeing of communities through the creation of job opportunities and sustainable livelihoods. The study is intended to assist the municipality to identify and address challenges that affect the successful implementation of LED projects. The following research aims to identify and assess the impacts that these projects have had on beneficiaries and the district as a whole. Furthermore the research aims to identify project successes as well as highlight shortcomings in order to enhance the economic impact of these projects in the future.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".