An Exploration of Social License to Operate (SLTO) Measurement in the Port Industry: The Case of North America
Bibliographic record
Abstract
In this paper, we develop exploratory research to improve the understanding of actual practices applied in the port industry relating to local communities’ perception measurement and public engagement, aiming at maintaining and fostering relationships with local communities. The application of such practices would allow port managing bodies to improve their strategic alignment with the needs and requirements of their local communities. To this end, we distributed a survey to North American port managing bodies and terminal operators. The survey, answered by 37 respondents, follows a structure defined by critical elements affecting stakeholder perceptions and acceptability in relation to a project or an ongoing business activity. The results disclose differences in social license to operate measurement and public engagement practices between port managing bodies and terminal operators. Furthermore, follow-up interviews were conducted with eight port managing bodies in order to capture the value added and the barriers to engage with local communities. Finally, the study enables benchmarking possibilities both within the sample and on a global level, giving an indication and assessment of the respondents’ competitive positions regarding stakeholder perceptions, communication, and engagement practices, and the steps to be taken in order to strengthen any strategic and competitive state.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".