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Article
Peer-Review Record

Shrinking the Variance in Experts’ “Classical” Weights Used in Expert Judgment Aggregation

Forecasting 2023, 5(3), 522-535; https://doi.org/10.3390/forecast5030029
Reviewer 1:
Reviewer 2: Anonymous
Forecasting 2023, 5(3), 522-535; https://doi.org/10.3390/forecast5030029
Submission received: 8 June 2023 / Revised: 11 August 2023 / Accepted: 21 August 2023 / Published: 23 August 2023
(This article belongs to the Special Issue Feature Papers of Forecasting 2023)

Round 1

Reviewer 1 Report

This is an interesting and generally well-written paper.  I suggest you reframe the paragraphs around lines 372-387 to better express the scientific outcomes rather than the feelings of disappointment of the researcher.  Perhaps "contrary to expectation..."  and "from this new conclude..."  Do your results boost confidence in current practices?

Note

less-> fewer line 386

form->from line 372

loosing -> losing line 430

The standard of English is appropriate

Author Response

Dear Reviewer,

Please refer to the uploaded pdf file.

Thank you in advance.

Best regards,

Gayan Dharmarathne.

Author Response File: Author Response.pdf

Reviewer 2 Report

This is an interesting and well-written paper on the hot topic of aggregating experts' judgments. I would like to thank the authors for their submission. I do have one, major comment. I would like to see the approach of variance shrinkage described by the authors applied on a larger and richer real data set, on top of what is already presented in the paper. This would give the readership more confidence and any conclusions would be more generalisable. 

Author Response

Dear Reviewer,

Please refer to the uploaded pdf file.

Thank you in advance.

Best regards,

Gayan Dharmarathne.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The authors have address my comments.

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