Analyzing referencing patterns in grey literature produced by influential global management consulting firms and international organizations
Bibliographic record
Abstract
Given the growing influence of non-academic organizations in the policy sphere, it is important to investigate the evidence both produced by and relied on by these organizations. Using citation analysis, a methodology primarily used in academic literature, we investigated the evidence base supporting the grey literature published by leading global management consulting firms (GMCFs) and international organizations (IOs). With the topic of the skills needed for the future of work as a case study, we collected 234 reports published by influential GMCFs and IOs over twenty years. By extracting references from the bibliographies of these reports we: 1) analyzed referencing patterns by measuring citation counts, institutional self-referencing and utilization of scholarly sources; 2) compared reference patterns across GMCFs and IOs; and 3) described the most influential sources. Overall, both GMCFs and IOs showed increasing reliance on grey literature, demonstrated high levels of self-referencing, and had considerable variation in the number of sources referred to. Across type of publishing organization, we found that IOs had better referencing practices than GMCFs. Our findings call into question the evidence-base behind the reports published by these policy actors. We emphasize the need to rely on strong academic literature to inform policy decisions around the future of work.
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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 | 0.071 | 0.348 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.168 | 0.158 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".