Bibliographic record
Abstract
In the forefront of the fourth industrial revolution is Artificial intelligence, better known as “AI.” As a frontier technology, AI is implementing deep and far-reaching changes into the way we work, play and live. These tools present numerous opportunities in solving issues of international development. Yet in spite of its infallible potential, the negative repercussions of AI driven change have become abundantly clear. These consequences will only be exacerbated in the Global South where there is a greater tendency for weak institutional capacity and governance. AI has the potential to threaten employment, human rights, democratic process and worsen economic dependency. The very nature of these tools--the ability to codify and reproduce patterns--must be met with responsible, ethical actors who ensure developmental goals will be met. Is AI4D the answer? This paper will illustrate the opportunities and risks of AI-driven development. I argue that technology can no longer be considered an inherent equalizer, and that the responsibility for fairness in the digital world must be championed by the international community. Finally, I will present possible steps policymakers can take to ensure true development in our data-driven 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 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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".