Conservation and Management of Protected Areas in China and India: A Literature Review (1990–2021)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Protected areas (PAs) are key to biodiversity conservation. As two highly populous and biodiverse countries, China and India are facing similar socioenvironmental pressures in the management of PAs. A comparative analysis of studies of PA policies in these two countries provides an objective assessment of policy concerns. This study involved a bibliometric analysis of studies of PA policies in China and India. Relevant publications were retrieved from the Web of Science and Scopus. The analysis was carried out using the Bibliometrix R Package, CiteSpace, and VOSviewer. The results indicate that PA policies studies in China are growing at an exponential rate, while Indian studies were cited significantly more often. “Environmental protection” was the main focus of the Chinese studies, with top keywords including “forest ecosystem” and “strategic approach”. In India, research was mainly focused on “wildlife management”, and the top keywords were “climate change” and “ecosystem service”. Studies from both countries were concerned with natural resource conservation and endangered species. Studies in India began relatively earlier and were more developed. India focused on people-related themes, while China emphasized strategic approaches. China is improving its system of PA and should learn from India to consider the relationship between environmental protection and people.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it