Bibliographic record
Abstract
<p style="text-align: justify; text-justify: inter-ideograph; line-height: 120%; layout-grid-mode: char; mso-layout-grid-align: none; punctuation-wrap: simple; margin: 12.0pt 0cm 6.0pt 0cm;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; color: black; mso-themecolor: text1;">Climate change poses an existential threat, disproportionately impacting marginalized communities. This article explores integrating environmental justice principles into Hong Kong legal education to empower future lawyers in combating the climate crisis. It highlights the importance of interdisciplinary approaches, experiential learning opportunities, and policy advocacy training. Case studies illustrate successful legal interventions and grassroots support strategies. The article emphasizes the evolution of professional ethics, balancing client interests with societal responsibilities. It proposes curriculum designs that incorporate real-world case studies, clinical experiences, and skill development in areas like legal drafting and climate litigation. By equipping Hong Kong law students with environmental justice perspectives, legal education can cultivate a generation of climate-conscious lawyers, driving transformative change towards a more equitable and sustainable future.</span></p>
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".