RRepoGEO

REPOGEO REPORT · LITE

zhijing-jin/nlp-phd-global-equality

Default branch main · commit bec39dc4 · scanned 5/13/2026, 5:52:56 AM

GitHub: 1,066 stars · 87 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface zhijing-jin/nlp-phd-global-equality, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    nlp-phd, ai-phd, career-guidance, academic-mentorship, global-equality, research-resources, natural-language-processing, artificial-intelligence, phd-application, academic-success
  • highlicense#2
    Add a LICENSE file to clarify usage terms

    Why:

    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Choose a common open-source license like MIT or Apache-2.0 to clarify how others can use and contribute to these resources.
  • mediumreadme#3
    Refine README's opening to emphasize its nature as a curated resource hub

    Why:

    CURRENT
    This repo originates with a wish to promote **Global Equality** for people who want to do a PhD in NLP, following the idea that mentorship programs are an effective way to fight against segregation, according to The Human Networks (Jackson, 2019). Specifically, we wish people from all over the world and with all types of backgrounds can share the same source of information, so that success will be a reward to those who are determined and hardworking, regardless of external contrainsts.
    COPY-PASTE FIX
    This repository is a curated collection of open resources and information designed to promote **Global Equality** for individuals pursuing PhDs in NLP/AI. It aims to provide a shared source of knowledge, mentorship insights, and career guidance to help aspiring and current PhD students succeed, regardless of their background or location.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface zhijing-jin/nlp-phd-global-equality
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle · recommended 2×
  2. Deep Learning Specialization by Andrew Ng · recommended 1×
  3. Natural Language Processing Specialization by deeplearning.ai · recommended 1×
  4. Machine Learning by Andrew Ng · recommended 1×
  5. Stanford CS224N · recommended 1×
  • CATEGORY QUERY
    Where can I find resources to prepare for a PhD in NLP or AI?
    you: not recommended
    AI recommended (in order):
    1. Deep Learning Specialization by Andrew Ng
    2. Natural Language Processing Specialization by deeplearning.ai
    3. Machine Learning by Andrew Ng
    4. Stanford CS224N
    5. Stanford CS231n
    6. MIT 6.034
    7. Speech and Language Processing by Daniel Jurafsky and James H. Martin
    8. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    9. Pattern Recognition and Machine Learning by Christopher M. Bishop
    10. Kaggle
    11. Hugging Face
    12. arXiv.org
    13. Python
    14. PyTorch (pytorch/pytorch)
    15. TensorFlow (tensorflow/tensorflow)
    16. scikit-learn (scikit-learn/scikit-learn)
    17. NumPy (numpy/numpy)
    18. Pandas (pandas-dev/pandas)

    AI recommended 18 alternatives but never named zhijing-jin/nlp-phd-global-equality. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are good resources for career guidance in AI and natural language processing?
    you: not recommended
    AI recommended (in order):
    1. Towards Data Science
    2. Kaggle
    3. LinkedIn Learning
    4. Coursera
    5. edX
    6. Deep Learning Specialization
    7. Natural Language Processing Specialization
    8. Lex Fridman Podcast
    9. Data Skeptic
    10. TWIML AI Podcast
    11. Reddit
    12. r/MachineLearning
    13. r/datascience
    14. r/learnmachinelearning
    15. O'Reilly Media

    AI recommended 15 alternatives but never named zhijing-jin/nlp-phd-global-equality. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of zhijing-jin/nlp-phd-global-equality?
    pass
    AI did not name zhijing-jin/nlp-phd-global-equality — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts zhijing-jin/nlp-phd-global-equality in production, what risks or prerequisites should they evaluate first?
    pass
    AI named zhijing-jin/nlp-phd-global-equality explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo zhijing-jin/nlp-phd-global-equality solve, and who is the primary audience?
    pass
    AI did not name zhijing-jin/nlp-phd-global-equality — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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zhijing-jin/nlp-phd-global-equality — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite