A Review and Reflection on the Spirit of Populus euphratica (Hu Yang) 2000-2020: An Analysis Using Cite Space Software
Bibliographic record
Abstract
The spirit of Populus euphratica embodies the spirit of hard struggle, self-improvement, and dedication rooted in the border regions. This paper focuses on the spirit of Populus euphratica, collecting research literature as the subject of study and using Cite Space software as an analytical tool to organize, summarize, and analyze relevant literature from academic journals published between 2000 and 2020. It aims to gain a comprehensive understanding of the achievements and shortcomings in the study of the spirit of Populus euphratica. Currently, related research primarily explores issues such as the formation process, connotation, core content, theme value, and contemporary value. These studies typically use three narrative frameworks: connotation formation, significance interpretation, and experiential enlightenment. Commonly used frameworks include educational research, value research, ideological and political research, and communication research. However, there are issues such as theoretical misalignment and lack of interdisciplinary cooperation. To address these issues, it is necessary to strengthen collaboration among different disciplines and the connection between academia and industry. The latest theoretical resources should be reasonably utilized.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".