Volume II: A Changing Third Sector Research Landscape—Progress or Pitfall?
Bibliographic record
Abstract
Abstract The last two decades have seen rapid advancements in data processing, collection, and analysis. While these have offered great opportunities for finding answers to enduring questions, the rise of new technologies for research purposes has raised the question of data neutrality, privacy, and equity. Expansions in data categorization, cleaning, and analysis require a broader understanding of the data collection process and its increased technification also increases the access gap to information. This introductory article focuses on the implications of new techniques and technologies to conduct research on the third sector and the nuances around data representation, equity, and justice in third-sector research. We also aim to identify new opportunities that the digitalization of the third sector has opened for nonprofit research, highlighting key methodological and ethical implications for future studies. We conclude by pushing third sector researchers to have more open discussions about issues of equity, inclusion, and representation in the ways we collect and analyze data.
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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 | 0.140 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.039 | 0.030 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.009 | 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".