Bibliographic record
Abstract
Let me begin by saying how much I agree with the overarching intent of John Lammers ’ essay. His manifest purpose is to highlight the ways in which a neo-institutional view of organizations might benefit from paying closer attention to communication theory. To accomplish this, Lammers offers a new construct, which he proposes as a means of focusing attention on the role of communication in replicating and diffusing institutional logics. In this regard, Lammers ’ essay succeeds. The nub of his argument is that researchers interested in institutions can improve their understanding of key processes of institutional reproduction by attending more carefully to the mechanisms and patterns of formal communication made on behalf of institu-tions. He terms this new construct “institutional messages, ” which are defined as communication “created in an inter-organizational environment that tran-scends particular settings, interactants and organizations ” (p. 19). Lammers goes on to describe a program of research that might emerge as a result of the new construct. This research will focus on categorizing the types of institu-tional messages, analyzing processes of sending and receiving them, measur-ing their endurance or strength, and so on. What I Like About “Institutional Messages” The power of this construct is that it directs sunlight on one of institutional theory’s biggest voids—that is, the absence of any mechanism that explains how institutional reproduction occurs. Implicit in much of institutional theory
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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.015 | 0.030 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.021 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 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".