Bibliographic record
Abstract
The leadership fundamentals identified by Bruyere (2015) miss a cornerstone of effective conservation leadership, namely a clear sense of purpose. Conservation often demands that we act fast (Martin et al. 2012), which requires clarity of purpose; “we are here to… (conserve species and ecosystems),” ahead of the long-term vision-building perspective emphasized by Bruyere (2015). All leaders, knowingly or not, work in a system; an ecosystem is obviously a system, but so is an NGO, a farm, a protected area, or a species recovery project. The best models of leadership focus on understanding the correct “purpose” of that system and how that influences the way people and work are managed (Scholtes 1998; Game et al. 2014). For example, priorities for an offshore island reserve are different to those for a wildlife corridor. Conservation recovery typically takes decades, yet conservation projects are often transient (Black et al. 2011), so although the future maybe unclear, conservation “purpose” must be enduring (i.e., “this programme exists to…”). Purpose guides our work today and tomorrow, our immediate priorities, our goals, our choices today, and our decisions for the future. A clear understanding of purpose is also a reference point for innovation and flexibility against the evolving demands of complex systems (Game et al. 2014). While there can be many visions (i.e., “what things will look like”) for a given purpose, there will be only one purpose for a given vision. A clear purpose will inform, test, and enable agreement on a relevant view of the future (from many possible permutations) to provide suitable direction. For example, black-footed ferret conservation in the 1990s focused on captive breeding and release of animals into sites in Wyoming, USA. Despite breeding successes which saved the species, few released ferrets survived and wild populations faltered; the vision was too limited and the purpose not delivered. Recognizing that habitat quality was critical, the vision was changed to include disease control, management of prey species, and land partnerships extending across eight states, plus Mexico and Canada (Black et al. 2011). The vision now engages a broader group of stakeholders and possibilities, but the purpose of conserving ferrets remains unchanged. While direction is important, understanding the underlying reason for a direction, its purpose, is paramount. Purpose is neither dependent nor solely identifiable with any one leader, so if the leader departs, the purpose of the work and the remaining team is unchanged. With a clear purpose in place, ego-driven or other value-laden perspectives are also less able to subvert more helpful commitments to species and ecosystems (Black & Copsey 2014). Furthermore, purposeful leaders avoid expending effort and resources on activities which do not deliver the principle focus of the program. Leaders set the tone through their behavior, goals, plans, vision, priorities, budgets, and performance measures (Black et al. 2011). All these elements should be guided by purpose; “how does this help us to… (conserve species and ecosystems)?” If conservation leaders make continued efforts to get this right, then the chances of success are improved.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".