Exploring Alternatives to APIs: Diverse Approaches to Data Collection
Bibliographic record
Abstract
In the context of generative AI’s rapid advancement across various industries, training models require large and comprehensive datasets to improve AI models, yielding more accurate and realistic outputs. This increased demand has reshaped the landscape of data accessibility and economics, particularly with Application Programming Interfaces (APIs). This shift has led to data providers and social media platforms enforcing new access restrictions. Such changes have created significant barriers for researchers, especially those in social science research, in acquiring data. This article addresses these issues by evaluating alternative data collection methods, focusing on their application in social research. It critically examines the strengths and weaknesses of these methods, underscoring their practicality and reliability. As AI continues to transform industries, this paper provides a vital guide for researchers, data analysts, and businesses to navigate the evolving dynamics of data collection, particularly in the context of social research.
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 | 0.175 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.018 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.026 | 0.043 |
| Open science | 0.008 | 0.020 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".