Golden reputation wanted for a gold producer. The case of the Rosia Montana Gold Corporation
Bibliographic record
Abstract
Inappropriate stakeholder communication often generates risks or even dangers to the organizations which are not aware of the importance of a proper flow of information to/from its publics. “Rosia Montana Gold Corporation”, a Canada-based company specialized in gold extraction, has initiated since 1996 a project to extract gold from the perimeter Rosia Montana (Western Carpathians, county of Alba, Romania). Although the technical documentation has been submitted back in 2004 to the authorities to be endorsed and approved, the approval is still pending due to a great amount of negative public perceptions often turned to hostile behaviors. In order to diminish this hostility, the company has started a huge communication campaign founded on factuality in its attempt to extract gold in Romania. However, the results are still unsatisfactory, even if the amount of negative perceptions has been lowered in a certain measure. In our paper we would like to analyze, based on the Situational Crisis Communication Theory (SCCT) and quantitative analysis methods, several influences produced by the organizational communication done so far over the organization itself, as well as over stakeholders such as the local community in Rosia Montana, public institutions, and non-governmental organizations.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".