Bord na Mona - A case study: The challenges of embedding an eco-entrepreneurial ethos
Bibliographic record
Abstract
This study is concerned with the emerging concept of ecological entrepreneurship, whereby a business is operated in an economically, environmentally and socially sustainable fashion. This research explores the relevance of this concept to Irish state-owned peat harvesting company Bord na Mona, with particular attention paid to its use of its cutaway bogs. International and European Union legislation which inform ecopreneurial activities are explored, and two models of best practice from Belarus and Canada for the after-use of post-industrially harvested peatlands are critically examined. This study explores current Irish cutaway bogland after-use and asks whether Bord na Mona's present use of its post-industrially harvested peatlands is sustainable from economic, environmental and social perspectives. In order to answer this research question, an appropriate methodology was devised using a qualitative case study approach, supplemented by a series of semi-structured interviews with key stakeholders. Conclusions are drawn and a number of recommendations are made. This research recommends inter alia that Bord na Mona work with national and international partners to develop sustainable sphagnum-based paludiculture regimes on its cutaway bogs. This research also recommends that Bord na Mona produces a business plan concerned with developing a carbon credit funded restoration and rehabilitation strategy for its cutaway bogs.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".