Organizational Autonomy and Survival in the United Nations System: A Comparative Case Study
Bibliographic record
Abstract
Chapter I: IntroductionAgency immortality, the notion that public institutions are extremely durable, resistant to change, and all but impossible to dismantle, was an idea that was commonly held in the study of public institutions.This idea was enshrined in Kaufman's empirical research on the population of public institutions, which found that the population overall remained relatively stable.However, the myth of agency immortality has since been thoroughly debunked.Researchers have found that, behind the seemingly stable population of institutions, vast currents of change exist, with institutions constantly changing, dissolving, and being replaced with new institutions.For example, in their study of the population of international governmental organizations (IGOs), Shanks et al. present several interesting insights into the dynamics of change in the population of IGOs, and their relationship to IGO membership among states.According to the authors, they set out to, "examine how countries affect the IGO population and how the IGO population in turn affects the choices that countries make about institutionalizing cooperation." 1 The authors find several trends within the time horizon they examined in the IGO population.A particularly intriguing aspect of their findings is the fact that IGO's belonging to the United Nations (UN) family of organizations have increased their share of the entire IGO population from 27 to 35 percent in eleven years.Even more astounding is the fact that the driving force behind such a large shift in population is an even more dramatic process of change.Shanks et al. reveal that, within these eleven years, over a quarter of UN affiliated organizations were discarded or demoted, yet more than twice as many new organizations were added to the ensemble of UN organizations, which ultimately led to such a significant net increase in the UN's share of the overall IGO population.Indeed, contrary to previously held notions of agency immortality, contemporary academic works seem to imply many indicators for organizational autonomy, to the likelihood of an agency's survival.Furthermore, we identify weak points in the academic literature found thus far and their implications for our analysis.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".