First i would thanks to kaggle for organizing this fun competition! some question about the Peer-Feedback:
\nSubmissions to the 2023 Kaggle AI Report competition will be assessed by an expert grading panel of seven Kaggle Grandmasters. Each of the seven topic areas has a dedicated grader who will be responsible for applying the evaluation rubric to essays submitted to their section. The Kaggle Team will also participate in the evaluation process in order to ensure a fair and standardized evaluation process. In the event of tied scores, the judging panel will stack rank submissions within the tied group.
First i would thanks to kaggle for organizing this fun competition! some question about the Peer-Feedback:
\nSubmissions to the 2023 Kaggle AI Report competition will be assessed by an expert grading panel of seven Kaggle Grandmasters. Each of the seven topic areas has a dedicated grader who will be responsible for applying the evaluation rubric to essays submitted to their section. The Kaggle Team will also participate in the evaluation process in order to ensure a fair and standardized evaluation process. In the event of tied scores, the judging panel will stack rank submissions within the tied group.
Because Kaggle have policy about originality and plagiarism, so this competition could not allowed to copy others notebook for ranking. I think the problem could be not enough teams for peer review?
", "rawMarkdown": "Because Kaggle have policy about originality and plagiarism, so this competition could not allowed to copy others notebook for ranking. I think the problem could be not enough teams for peer review?", "votes": null }, { "id": "2255891", "postDate": "05/12/2023 04:43:42", "content": "Q1: Participants must make the notebooks containing their essays public no later than July 5th, 2023 (the start of the peer-feedback period). Many submissions will be kept private until the deadline, but if teams start sharing their public notebooks early, it might be a really nice way to share ideas.
\nQ2: In the peer-feedback instructions we request \"Try to not provide feedback on notebooks that already have three constructive comments. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\". There is no penalty if too many or two few people provide feedback about your submission.
\nQ3: When you provide feedback to your peer it does not directly impact either their score or their final leaderboard position. Final leaderboard positions are determined by the expert grading panel in a process that is separate from the peer feedback.
\n(For future reference, there is a pinned Question & Answer thread where you should find the fastest response times for questions directed to the Kaggle Team.)
", "rawMarkdown": "Q1: Participants must make the notebooks containing their essays public no later than July 5th, 2023 (the start of the peer-feedback period). Many submissions will be kept private until the deadline, but if teams start sharing their public notebooks early, it might be a really nice way to share ideas.\n\nQ2: In the [peer-feedback instructions](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions) we request \"Try to not provide feedback on notebooks that already have three constructive comments. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\". There is no penalty if too many or two few people provide feedback about your submission.\n\nQ3: When you provide feedback to your peer it does not directly impact either their score or their final leaderboard position. Final leaderboard positions are determined by the expert grading panel in a process that is separate from the peer feedback.\n\n(For future reference, there is a pinned [Question & Answer thread](https://www.kaggle.com/competitions/2023-kaggle-ai-report/discussion/400084) where you should find the fastest response times for questions directed to the Kaggle Team.)", "votes": null }, { "id": "2255894", "postDate": "05/12/2023 04:49:23", "content": "Participants are encouraged to review any peer essay that they'd like (prioritizing their own topic), and by July 5th (the Code Listing) should be full of public notebooks to choose from. There is no penalty if your own essay receives too many or too few reviews.
\nHopefully that helps, please feel free to keep asking questions if things are still unclear!
", "rawMarkdown": "Participants are encouraged to review any peer essay that they'd like (prioritizing their own topic), and by July 5th (the [Code Listing](https://www.kaggle.com/competitions/2023-kaggle-ai-report/code)) should be full of public notebooks to choose from. There is no penalty if your own essay receives too many or too few reviews. \n\nHopefully that helps, please feel free to keep asking questions if things are still unclear!", "votes": null }, { "id": "2255945", "postDate": "05/12/2023 06:06:01", "content": "Thanks for your detailed explanation!
", "rawMarkdown": "Thanks for your detailed explanation!", "votes": null }, { "id": "2266350", "postDate": "05/19/2023 23:59:57", "content": "hello everybody ! please, how can i get the three feedback_urls?
\nthanks.
", "rawMarkdown": "hello everybody ! please, how can i get the three feedback_urls?\n\nthanks.", "votes": null } ], "comments": [ { "id": 2255861, "author_name": "dwchen", "author_url": "", "post_date": "05/12/2023 03:57:03", "content": "Because Kaggle have policy about originality and plagiarism, so this competition could not allowed to copy others notebook for ranking. I think the problem could be not enough teams for peer review?
", "votes": null, "replies": [ { "id": 2255894, "author_name": "paultimothymooney", "author_url": "", "post_date": "05/12/2023 04:49:23", "content": "Participants are encouraged to review any peer essay that they'd like (prioritizing their own topic), and by July 5th (the Code Listing) should be full of public notebooks to choose from. There is no penalty if your own essay receives too many or too few reviews.
\nHopefully that helps, please feel free to keep asking questions if things are still unclear!
", "votes": null, "replies": [] } ] }, { "id": 2255891, "author_name": "paultimothymooney", "author_url": "", "post_date": "05/12/2023 04:43:42", "content": "Q1: Participants must make the notebooks containing their essays public no later than July 5th, 2023 (the start of the peer-feedback period). Many submissions will be kept private until the deadline, but if teams start sharing their public notebooks early, it might be a really nice way to share ideas.
\nQ2: In the peer-feedback instructions we request \"Try to not provide feedback on notebooks that already have three constructive comments. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\". There is no penalty if too many or two few people provide feedback about your submission.
\nQ3: When you provide feedback to your peer it does not directly impact either their score or their final leaderboard position. Final leaderboard positions are determined by the expert grading panel in a process that is separate from the peer feedback.
\n(For future reference, there is a pinned Question & Answer thread where you should find the fastest response times for questions directed to the Kaggle Team.)
", "votes": null, "replies": [ { "id": 2255945, "author_name": "evilpsycho42", "author_url": "", "post_date": "05/12/2023 06:06:01", "content": "Thanks for your detailed explanation!
", "votes": null, "replies": [] } ] }, { "id": 2266350, "author_name": "raphallouka", "author_url": "", "post_date": "05/19/2023 23:59:57", "content": "hello everybody ! please, how can i get the three feedback_urls?
\nthanks.
", "votes": null, "replies": [] } ], "raw_markdown_by_id": { "2255835": "First i would thanks to kaggle for organizing this fun competition! some question about the Peer-Feedback:\n\n1. As for the effectiveness of the evaluation, with the update of the notebook version, the previous evaluation may not be applicable. This concern may make participators tent to public the notebook in the final stage of the competition, resulting in a lack of vitality during competition?\n2. If a notebook has more than 3 peer-feedbacks, how to judge?\n3. How does peer review affect the final GM score?\n\n`Submissions to the 2023 Kaggle AI Report competition will be assessed by an expert grading panel of seven Kaggle Grandmasters. Each of the seven topic areas has a dedicated grader who will be responsible for applying the evaluation rubric to essays submitted to their section. The Kaggle Team will also participate in the evaluation process in order to ensure a fair and standardized evaluation process. In the event of tied scores, the judging panel will stack rank submissions within the tied group.`", "2255861": "Because Kaggle have policy about originality and plagiarism, so this competition could not allowed to copy others notebook for ranking. I think the problem could be not enough teams for peer review?", "2255891": "Q1: Participants must make the notebooks containing their essays public no later than July 5th, 2023 (the start of the peer-feedback period). Many submissions will be kept private until the deadline, but if teams start sharing their public notebooks early, it might be a really nice way to share ideas.\n\nQ2: In the [peer-feedback instructions](https://www.kaggle.com/competitions/2023-kaggle-ai-report/overview/peer-feedback-instructions) we request \"Try to not provide feedback on notebooks that already have three constructive comments. If every notebook on your topic already has 3 or more comments providing peer-feedback, you can instead provide peer-feedback on a notebook from a different topic that does not yet have 3 comments on it.\". There is no penalty if too many or two few people provide feedback about your submission.\n\nQ3: When you provide feedback to your peer it does not directly impact either their score or their final leaderboard position. Final leaderboard positions are determined by the expert grading panel in a process that is separate from the peer feedback.\n\n(For future reference, there is a pinned [Question & Answer thread](https://www.kaggle.com/competitions/2023-kaggle-ai-report/discussion/400084) where you should find the fastest response times for questions directed to the Kaggle Team.)", "2255894": "Participants are encouraged to review any peer essay that they'd like (prioritizing their own topic), and by July 5th (the [Code Listing](https://www.kaggle.com/competitions/2023-kaggle-ai-report/code)) should be full of public notebooks to choose from. There is no penalty if your own essay receives too many or too few reviews. \n\nHopefully that helps, please feel free to keep asking questions if things are still unclear!", "2255945": "Thanks for your detailed explanation!", "2266350": "hello everybody ! please, how can i get the three feedback_urls?\n\nthanks." }, "source": "meta" }