Maturity as a way forward for improving organizations’ communication evaluation and measurement practices
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose an explication of the concept of “maturity,” as it applies to communication evaluation and measurement (E&M) practice, along with contextualization of recent maturity model adoption within academic and professional communities. Design/methodology/approach Drawing from previous work on maturity models within other fields, recent communication scholarship and industry practice, this paper fills a gap in the literature by offering a theoretical conceptualization of communication E&M maturity, including the construct’s core dimensions and sub-dimensions. Findings Communication E&M maturity is conceptualized into four essential elements: holistic approach, investment, alignment and culture. The contribution of E&M efforts is represented as the direct support of corporate strategy, and ultimately increased value, from the communications function. Operational elements of maturity include levels of analysis, time, budget, tools, skills, process, integration, motivations, relationships and standards. Originality/value In exploring the factors necessary for “mature” E&M programs, and specifically emphasizing the need for a holistic approach, along with sufficient investment and alignment, and conducive cultural factors, the research builds upon existing work examining how communication can serve to inform corporate strategy and create value for an organization. Greater understanding and application of the maturity concept has the potential to advance the field by increasing both accountability and credibility for the work done by the communications function.
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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 | 0.359 | 0.433 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.019 | 0.035 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.012 |
| 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".